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Sample records for active learning algorithms

  1. An Experimental Method for the Active Learning of Greedy Algorithms

    ERIC Educational Resources Information Center

    Velazquez-Iturbide, J. Angel

    2013-01-01

    Greedy algorithms constitute an apparently simple algorithm design technique, but its learning goals are not simple to achieve.We present a didacticmethod aimed at promoting active learning of greedy algorithms. The method is focused on the concept of selection function, and is based on explicit learning goals. It mainly consists of an…

  2. Active structured learning for cell tracking: algorithm, framework, and usability.

    PubMed

    Lou, Xinghua; Schiegg, Martin; Hamprecht, Fred A

    2014-04-01

    One distinguishing property of life is its temporal dynamics, and it is hence only natural that time lapse experiments play a crucial role in modern biomedical research areas such as signaling pathways, drug discovery or developmental biology. Such experiments yield a very large number of images that encode complex cellular activities, and reliable automated cell tracking emerges naturally as a prerequisite for further quantitative analysis. However, many existing cell tracking methods are restricted to using only a small number of features to allow for manual tweaking. In this paper, we propose a novel cell tracking approach that embraces a powerful machine learning technique to optimize the tracking parameters based on user annotated tracks. Our approach replaces the tedious parameter tuning with parameter learning and allows for the use of a much richer set of complex tracking features, which in turn affords superior prediction accuracy. Furthermore, we developed an active learning approach for efficient training data retrieval, which reduces the annotation effort to only 17%. In practical terms, our approach allows life science researchers to inject their expertise in a more intuitive and direct manner. This process is further facilitated by using a glyph visualization technique for ground truth annotation and validation. Evaluation and comparison on several publicly available benchmark sequences show significant performance improvement over recently reported approaches. Code and software tools are provided to the public.

  3. Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Kanevski, Mikhail; Volpi, Michele; Copa, Loris

    2010-05-01

    The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of

  4. An Active Learning Algorithm for Control of Epidural Electrostimulation.

    PubMed

    Desautels, Thomas A; Choe, Jaehoon; Gad, Parag; Nandra, Mandheerej S; Roy, Roland R; Zhong, Hui; Tai, Yu-Chong; Edgerton, V Reggie; Burdick, Joel W

    2015-10-01

    Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm's performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions' results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy.

  5. An Active Learning Algorithm for Control of Epidural Electrostimulation

    PubMed Central

    Desautels, Thomas A.; Nandra, Mandheerej S.; Roy, Roland R.; Zhong, Hui; Tai, Yu-Chong; Edgerton, V. Reggie; Burdick, Joel W.

    2015-01-01

    Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm's performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved, intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained, autonomous operations (10 session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions’ results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy. PMID:25974925

  6. Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms

    PubMed Central

    Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

    2014-01-01

    Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel. PMID:24795875

  7. Online Pairwise Learning Algorithms.

    PubMed

    Ying, Yiming; Zhou, Ding-Xuan

    2016-04-01

    Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

  8. Algorithm-development activities

    NASA Technical Reports Server (NTRS)

    Carder, Kendall L.

    1994-01-01

    The task of algorithm-development activities at USF continues. The algorithm for determining chlorophyll alpha concentration, (Chl alpha) and gelbstoff absorption coefficient for SeaWiFS and MODIS-N radiance data is our current priority.

  9. Storage capacity of the Tilinglike Learning Algorithm

    NASA Astrophysics Data System (ADS)

    Buhot, Arnaud; Gordon, Mirta B.

    2001-02-01

    The storage capacity of an incremental learning algorithm for the parity machine, the Tilinglike Learning Algorithm, is analytically determined in the limit of a large number of hidden perceptrons. Different learning rules for the simple perceptron are investigated. The usual Gardner-Derrida rule leads to a storage capacity close to the upper bound, which is independent of the learning algorithm considered.

  10. A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks.

    PubMed

    Zhang, Xu; Foderaro, Greg; Henriquez, Craig; Ferrari, Silvia

    2016-12-22

    Recent developments in neural stimulation and recording technologies are providing scientists with the ability of recording and controlling the activity of individual neurons in vitro or in vivo, with very high spatial and temporal resolution. Tools such as optogenetics, for example, are having a significant impact in the neuroscience field by delivering optical firing control with the precision and spatiotemporal resolution required for investigating information processing and plasticity in biological brains. While a number of training algorithms have been developed to date for spiking neural network (SNN) models of biological neuronal circuits, exiting methods rely on learning rules that adjust the synaptic strengths (or weights) directly, in order to obtain the desired network-level (or functional-level) performance. As such, they are not applicable to modifying plasticity in biological neuronal circuits, in which synaptic strengths only change as a result of pre- and post-synaptic neuron firings or biological mechanisms beyond our control. This paper presents a weight-free training algorithm that relies solely on adjusting the spatiotemporal delivery of neuron firings in order to optimize the network performance. The proposed weight-free algorithm does not require any knowledge of the SNN model or its plasticity mechanisms. As a result, this training approach is potentially realizable in vitro or in vivo via neural stimulation and recording technologies, such as optogenetics and multielectrode arrays, and could be utilized to control plasticity at multiple scales of biological neuronal circuits. The approach is demonstrated by training SNNs with hundreds of units to control a virtual insect navigating in an unknown environment.

  11. Learning with the ratchet algorithm.

    SciTech Connect

    Hush, D. R.; Scovel, James C.

    2003-01-01

    This paper presents a randomized algorithm called Ratchet that asymptotically minimizes (with probability 1) functions that satisfy a positive-linear-dependent (PLD) property. We establish the PLD property and a corresponding realization of Ratchet for a generalized loss criterion for both linear machines and linear classifiers. We describe several learning criteria that can be obtained as special cases of this generalized loss criterion, e.g. classification error, classification loss and weighted classification error. We also establish the PLD property and a corresponding realization of Ratchet for the Neyman-Pearson criterion for linear classifiers. Finally we show how, for linear classifiers, the Ratchet algorithm can be derived as a modification of the Pocket algorithm.

  12. Efficient Learning Algorithms with Limited Information

    ERIC Educational Resources Information Center

    De, Anindya

    2013-01-01

    The thesis explores efficient learning algorithms in settings which are more restrictive than the PAC model of learning (Valiant) in one of the following two senses: (i) The learning algorithm has a very weak access to the unknown function, as in, it does not get labeled samples for the unknown function (ii) The error guarantee required from the…

  13. Approximate learning algorithm in Boltzmann machines.

    PubMed

    Yasuda, Muneki; Tanaka, Kazuyuki

    2009-11-01

    Boltzmann machines can be regarded as Markov random fields. For binary cases, they are equivalent to the Ising spin model in statistical mechanics. Learning systems in Boltzmann machines are one of the NP-hard problems. Thus, in general we have to use approximate methods to construct practical learning algorithms in this context. In this letter, we propose new and practical learning algorithms for Boltzmann machines by using the belief propagation algorithm and the linear response approximation, which are often referred as advanced mean field methods. Finally, we show the validity of our algorithm using numerical experiments.

  14. Learning Intelligent Genetic Algorithms Using Japanese Nonograms

    ERIC Educational Resources Information Center

    Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

    2012-01-01

    An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

  15. Algorithmization in Learning and Instruction.

    ERIC Educational Resources Information Center

    Landa, L. N.

    An introduction to the theory of algorithms reviews the theoretical issues of teaching algorithms, the logical and psychological problems of devising algorithms of identification, and the selection of efficient algorithms; and then relates all of these to the classroom teaching process. It also descirbes some major research on the effectiveness of…

  16. Generation of attributes for learning algorithms

    SciTech Connect

    Hu, Yuh-Jyh; Kibler, D.

    1996-12-31

    Inductive algorithms rely strongly on their representational biases. Constructive induction can mitigate representational inadequacies. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present the results which demonstrate the effectiveness of GALA on artificial and real domains for several learners: C4.5, CN2, perceptron and backpropagation.

  17. An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance

    ERIC Educational Resources Information Center

    Grivokostopoulou, Foteini; Perikos, Isidoros; Hatzilygeroudis, Ioannis

    2017-01-01

    In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active…

  18. Visualizing output for a data learning algorithm

    NASA Astrophysics Data System (ADS)

    Carson, Daniel; Graham, James; Ternovskiy, Igor

    2016-05-01

    This paper details the process we went through to visualize the output for our data learning algorithm. We have been developing a hierarchical self-structuring learning algorithm based around the general principles of the LaRue model. One example of a proposed application of this algorithm would be traffic analysis, chosen because it is conceptually easy to follow and there is a significant amount of already existing data and related research material with which to work with. While we choose the tracking of vehicles for our initial approach, it is by no means the only target of our algorithm. Flexibility is the end goal, however, we still need somewhere to start. To that end, this paper details our creation of the visualization GUI for our algorithm, the features we included and the initial results we obtained from our algorithm running a few of the traffic based scenarios we designed.

  19. Active Learning with Irrelevant Examples

    NASA Technical Reports Server (NTRS)

    Mazzoni, Dominic; Wagstaff, Kiri L.; Burl, Michael

    2006-01-01

    Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

  20. A Learning Algorithm for Multimodal Grammar Inference.

    PubMed

    D'Ulizia, A; Ferri, F; Grifoni, P

    2011-12-01

    The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.

  1. Learning Activities.

    ERIC Educational Resources Information Center

    Tipton, Tom, Ed.

    1983-01-01

    Presents a flow chart for naming inorganic compounds. Although it is not necessary for students to memorize rules, preliminary skills needed before using the chart are outlined. Also presents an activity in which the mass of an imaginary atom is determined using lead shot, Petri dishes, and a platform balance. (JN)

  2. Learning algorithms for human-machine interfaces.

    PubMed

    Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

    2009-05-01

    The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions. The high-dimensional glove signals remotely control the joint angles of a simulated planar two-link arm on a computer screen, which is used to acquire targets. A machine learning algorithm was applied to adaptively change the transformation between finger motion and the simulated robot arm. This algorithm was either LMS gradient descent or the Moore-Penrose (MP) pseudoinverse transformation. Both algorithms modified the glove-to-joint angle map so as to reduce the endpoint errors measured in past performance. The MP group performed worse than the control group (subjects not exposed to any machine learning), while the LMS group outperformed the control subjects. However, the LMS subjects failed to achieve better generalization than the control subjects, and after extensive training converged to the same level of performance as the control subjects. These results highlight the limitations of coadaptive learning using only endpoint error reduction.

  3. Paradigms for Realizing Machine Learning Algorithms.

    PubMed

    Agneeswaran, Vijay Srinivas; Tonpay, Pranay; Tiwary, Jayati

    2013-12-01

    The article explains the three generations of machine learning algorithms-with all three trying to operate on big data. The first generation tools are SAS, SPSS, etc., while second generation realizations include Mahout and RapidMiner (that work over Hadoop), and the third generation paradigms include Spark and GraphLab, among others. The essence of the article is that for a number of machine learning algorithms, it is important to look beyond the Hadoop's Map-Reduce paradigm in order to make them work on big data. A number of promising contenders have emerged in the third generation that can be exploited to realize deep analytics on big data.

  4. The kinetic activation-relaxation technique: an off-lattice, self-learning kinetic Monte Carlo algorithm with on-the-fly event search

    NASA Astrophysics Data System (ADS)

    Mousseau, Nomand

    2012-02-01

    While kinetic Monte Carlo algorithm has been proposed almost 40 years ago, its application in materials science has been mostly limited to lattice-based motion due to the difficulties associated with identifying new events and building usable catalogs when atoms moved into off-lattice position. Here, I present the kinetic activation-relaxation technique (kinetic ART) is an off-lattice, self-learning kinetic Monte Carlo algorithm with on-the-fly event search [1]. It combines ART nouveau [2], a very efficient unbiased open-ended activated method for finding transition states, with a topological classification [3] that allows a discrete cataloguing of local environments in complex systems, including disordered materials. In kinetic ART, local topologies are first identified for all atoms in a system. ART nouveau event searches are then launched for new topologies, building an extensive catalog of barriers and events. Next, all low energy events are fully reconstructed and relaxed, allowing to take complete account of elastic effects in the system's kinetics. Using standard kinetic Monte Carlo, the clock is brought forward and an event is then selected and applied before a new search for topologies is launched. In addition to presenting the various elements of the algorithm, I will discuss three recent applications to ion-bombarded silicon, defect diffusion in Fe and structural relaxation in amorphous silicon.[4pt] This work was done in collaboration with Laurent Karim B'eland, Peter Brommer, Fedwa El-Mellouhi, Jean-Francois Joly and Laurent Lewis.[4pt] [1] F. El-Mellouhi, N. Mousseau and L.J. Lewis, Phys. Rev. B. 78, 153202 (2008); L.K. B'eland et al., Phys. Rev. E 84, 046704 (2011).[2] G.T. Barkema and N. Mousseau, Phys. Rev. Lett. 77, 4358 (1996); E. Machado-Charry et al., J. Chem Phys. 135, 034102, (2011).[3] B.D. McKay, Congressus Numerantium 30, 45 (1981).

  5. Active Learning Methods

    ERIC Educational Resources Information Center

    Zayapragassarazan, Z.; Kumar, Santosh

    2012-01-01

    Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

  6. Noise-enhanced clustering and competitive learning algorithms.

    PubMed

    Osoba, Osonde; Kosko, Bart

    2013-01-01

    Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning.

  7. Active Learning with Irrelevant Examples

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri; Mazzoni, Dominic

    2009-01-01

    An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item

  8. Storage capacity of a constructive learning algorithm

    NASA Astrophysics Data System (ADS)

    Buhot, Arnaud; Gordon, Mirta B.

    2000-03-01

    Upper and lower bounds for the typical storage capacity of a constructive algorithm, the tilinglike learning algorithm for the parity machine (Biehl M and Opper M 1991 Phys. Rev. A 44 6888), are determined in the asymptotic limit of large training set sizes. The properties of a perceptron with threshold, learning a training set of patterns having a biased distribution of targets, needed as an intermediate step in the capacity calculation, are determined analytically. The lower bound for the capacity, determined with a cavity method, is proportional to the number of hidden units. The upper bound, obtained with the hypothesis of replica symmetry, is close to the one predicted by Mitchinson and Durbin (1989 Biol. Cybern. 60 345).

  9. Accuracy estimation for supervised learning algorithms

    SciTech Connect

    Glover, C.W.; Oblow, E.M.; Rao, N.S.V.

    1997-04-01

    This paper illustrates the relative merits of three methods - k-fold Cross Validation, Error Bounds, and Incremental Halting Test - to estimate the accuracy of a supervised learning algorithm. For each of the three methods we point out the problem they address, some of the important assumptions that are based on, and illustrate them through an example. Finally, we discuss the relative advantages and disadvantages of each method.

  10. Learning algorithms for perceptrons from statistical physics

    NASA Astrophysics Data System (ADS)

    Gordon, Mirta B.; Peretto, Pierre; Berchier, Dominique

    1993-02-01

    Learning algorithms for perceptrons are deduced from statistical mechanics. Thermodynamical quantities are used as cost functions which may be extremalized by gradient dynamics to find the synaptic efficacies that store the learning set of patterns. The learning rules so obtained are classified in two categories, following the statistics used to derive the cost functions, namely, Boltzmann statistics, and Fermi statistics. In the limits of zero or infinite temperatures some of the rules behave like already known algorithms, but new strategies for learning are obtained at finite temperatures, which minimize the number of errors on the training set. Nous déduisons des algorithmes d'apprentissage pour des perceptrons à partir de considérations de mécanique statistique. Des quantités thermodynamiques sont considérées comme des fonctions de coût, dont on obtient, par une dynamique de gradient, les efficacités synaptiques qui apprennent l'ensemble d'apprentissage. Les règles ainsi obtenues sont classées en deux catégories suivant les statistiques, de Boltzmann ou de Fermi, utilisées pour dériver les fonctions de coût. Dans les limites de températures nulle ou infinie, la plupart des règles trouvées tendent vers les algorithmes connus, mais à température finie on trouve des stratégies nouvelles, qui minimisent le nombre d'erreurs dans l'ensemble d'apprentissage.

  11. Safe Exploration Algorithms for Reinforcement Learning Controllers.

    PubMed

    Mannucci, Tommaso; van Kampen, Erik-Jan; de Visser, Cornelis; Chu, Qiping

    2017-02-06

    Self-learning approaches, such as reinforcement learning, offer new possibilities for autonomous control of uncertain or time-varying systems. However, exploring an unknown environment under limited prediction capabilities is a challenge for a learning agent. If the environment is dangerous, free exploration can result in physical damage or in an otherwise unacceptable behavior. With respect to existing methods, the main contribution of this paper is the definition of a new approach that does not require global safety functions, nor specific formulations of the dynamics or of the environment, but relies on interval estimation of the dynamics of the agent during the exploration phase, assuming a limited capability of the agent to perceive the presence of incoming fatal states. Two algorithms are presented with this approach. The first is the Safety Handling Exploration with Risk Perception Algorithm (SHERPA), which provides safety by individuating temporary safety functions, called backups. SHERPA is shown in a simulated, simplified quadrotor task, for which dangerous states are avoided. The second algorithm, denominated OptiSHERPA, can safely handle more dynamically complex systems for which SHERPA is not sufficient through the use of safety metrics. An application of OptiSHERPA is simulated on an aircraft altitude control task.

  12. Generalized SMO algorithm for SVM-based multitask learning.

    PubMed

    Cai, Feng; Cherkassky, Vladimir

    2012-06-01

    Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

  13. TAO-robust backpropagation learning algorithm.

    PubMed

    Pernía-Espinoza, Alpha V; Ordieres-Meré, Joaquín B; Martínez-de-Pisón, Francisco J; González-Marcos, Ana

    2005-03-01

    In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model tau-estimates [introduced by Tabatabai, M. A. Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two psi functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example.

  14. Learning algorithms for stack filter classifiers

    SciTech Connect

    Porter, Reid B; Hush, Don; Zimmer, Beate G

    2009-01-01

    Stack Filters define a large class of increasing filter that is used widely in image and signal processing. The motivations for using an increasing filter instead of an unconstrained filter have been described as: (1) fast and efficient implementation, (2) the relationship to mathematical morphology and (3) more precise estimation with finite sample data. This last motivation is related to methods developed in machine learning and the relationship was explored in an earlier paper. In this paper we investigate this relationship by applying Stack Filters directly to classification problems. This provides a new perspective on how monotonicity constraints can help control estimation and approximation errors, and also suggests several new learning algorithms for Boolean function classifiers when they are applied to real-valued inputs.

  15. Fast linear algorithms for machine learning

    NASA Astrophysics Data System (ADS)

    Lu, Yichao

    Nowadays linear methods like Regression, Principal Component Analysis and Canonical Correlation Analysis are well understood and widely used by the machine learning community for predictive modeling and feature generation. Generally speaking, all these methods aim at capturing interesting subspaces in the original high dimensional feature space. Due to the simple linear structures, these methods all have a closed form solution which makes computation and theoretical analysis very easy for small datasets. However, in modern machine learning problems it's very common for a dataset to have millions or billions of features and samples. In these cases, pursuing the closed form solution for these linear methods can be extremely slow since it requires multiplying two huge matrices and computing inverse, inverse square root, QR decomposition or Singular Value Decomposition (SVD) of huge matrices. In this thesis, we consider three fast algorithms for computing Regression and Canonical Correlation Analysis approximate for huge datasets.

  16. In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms.

    PubMed

    Paduszyński, Kamil

    2016-08-22

    The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many applications of ILs, particularly in separations. Three new models are proposed, each of them based on distinct machine learning algorithm: stepwise multiple linear regression (SWMLR), feed-forward artificial neural network (FFANN), and least-squares support vector machine (LSSVM). The models were established based on the most comprehensive γ(∞) data bank reported so far (>34 000 data points for 188 ILs and 128 solutes). Following the paper published previously [J. Chem. Inf. Model 2014, 54, 1311-1324], the ILs were treated in terms of group contributions, whereas the Abraham solvation parameters were used to quantify an impact of solute structure. Temperature is also included in the input data of the models so that they can be utilized to obtain temperature-dependent data and thus related thermodynamic functions. Both internal and external validation techniques were applied to assess the statistical significance and explanatory power of the final correlations. A comparative study of the overall performance of the investigated SWMLR/FFANN/LSSVM approaches is presented in terms of root-mean-square error and average absolute relative deviation between calculated and experimental γ(∞), evaluated for different families of ILs and solutes, as well as between calculated and experimental infinite dilution selectivity for separation problems benzene from n-hexane and thiophene from n-heptane. LSSVM is shown to be a method with the lowest values of both training and generalization errors. It is finally demonstrated that the established models exhibit an improved accuracy compared to the state-of-the-art model, namely, temperature-dependent group contribution linear solvation energy relationship, published in 2011 [J. Chem

  17. Information Theory, Inference and Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Mackay, David J. C.

    2003-10-01

    Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

  18. Technology Learning Activities I.

    ERIC Educational Resources Information Center

    International Technology Education Association, Reston, VA.

    This guide contains 30 technology learning activities. Activities may contain all or some of the following: an introduction, objectives, materials and equipment, challenges, limitations, notes and investigations, resources and references used, and evaluation ideas. Activity titles are: (1) Occupations in Construction Technology; (2) Designing a…

  19. A Competency-Based Guided-Learning Algorithm Applied on Adaptively Guiding E-Learning

    ERIC Educational Resources Information Center

    Hsu, Wei-Chih; Li, Cheng-Hsiu

    2015-01-01

    This paper presents a new algorithm called competency-based guided-learning algorithm (CBGLA), which can be applied on adaptively guiding e-learning. Computational process analysis and mathematical derivation of competency-based learning (CBL) were used to develop the CBGLA. The proposed algorithm could generate an effective adaptively guiding…

  20. Active Learning in the Era of Big Data

    SciTech Connect

    Jamieson, Kevin; Davis, IV, Warren L.

    2015-10-01

    Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.

  1. Research on machine learning framework based on random forest algorithm

    NASA Astrophysics Data System (ADS)

    Ren, Qiong; Cheng, Hui; Han, Hai

    2017-03-01

    With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

  2. Active Processor Scheduling Using Evolutionary Algorithms

    DTIC Science & Technology

    2002-12-01

    xiii Active Processor Scheduling Using Evolutionary Algorithms I. Introduction A distributed system offers the ability to run applications across...calculations are made. This model is sometimes referred to as a form of the island model of evolutionary computation because each population is evolved... Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic Algorithms and Evolutionary Computation , New York: Kluwer Academic Publishers, 2002

  3. Gradient descent learning algorithm overview: a general dynamical systems perspective.

    PubMed

    Baldi, P

    1995-01-01

    Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.

  4. Location-Aware Mobile Learning of Spatial Algorithms

    ERIC Educational Resources Information Center

    Karavirta, Ville

    2013-01-01

    Learning an algorithm--a systematic sequence of operations for solving a problem with given input--is often difficult for students due to the abstract nature of the algorithms and the data they process. To help students understand the behavior of algorithms, a subfield in computing education research has focused on algorithm…

  5. Automated training for algorithms that learn from genomic data.

    PubMed

    Cilingir, Gokcen; Broschat, Shira L

    2015-01-01

    Supervised machine learning algorithms are used by life scientists for a variety of objectives. Expert-curated public gene and protein databases are major resources for gathering data to train these algorithms. While these data resources are continuously updated, generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction. In this paper, we propose a new model of operation for supervised machine learning algorithms that learn from genomic data. By defining these algorithms in a pipeline in which the training data gathering procedure and the learning process are automated, one can create a system that generates a classifier or predictor using information available from public resources. The proposed model is explained using three case studies on SignalP, MemLoci, and ApicoAP in which existing machine learning models are utilized in pipelines. Given that the vast majority of the procedures described for gathering training data can easily be automated, it is possible to transform valuable machine learning algorithms into self-evolving learners that benefit from the ever-changing data available for gene products and to develop new machine learning algorithms that are similarly capable.

  6. Active learning: learning a motor skill without a coach.

    PubMed

    Huang, Vincent S; Shadmehr, Reza; Diedrichsen, Jörn

    2008-08-01

    When we learn a new skill (e.g., golf) without a coach, we are "active learners": we have to choose the specific components of the task on which to train (e.g., iron, driver, putter, etc.). What guides our selection of the training sequence? How do choices that people make compare with choices made by machine learning algorithms that attempt to optimize performance? We asked subjects to learn the novel dynamics of a robotic tool while moving it in four directions. They were instructed to choose their practice directions to maximize their performance in subsequent tests. We found that their choices were strongly influenced by motor errors: subjects tended to immediately repeat an action if that action had produced a large error. This strategy was correlated with better performance on test trials. However, even when participants performed perfectly on a movement, they did not avoid repeating that movement. The probability of repeating an action did not drop below chance even when no errors were observed. This behavior led to suboptimal performance. It also violated a strong prediction of current machine learning algorithms, which solve the active learning problem by choosing a training sequence that will maximally reduce the learner's uncertainty about the task. While we show that these algorithms do not provide an adequate description of human behavior, our results suggest ways to improve human motor learning by helping people choose an optimal training sequence.

  7. Learning Activity Package, Algebra.

    ERIC Educational Resources Information Center

    Evans, Diane

    A set of ten teacher-prepared Learning Activity Packages (LAPs) in beginning algebra and nine in intermediate algebra, these units cover sets, properties of operations, number systems, open expressions, solution sets of equations and inequalities in one and two variables, exponents, factoring and polynomials, relations and functions, radicals,…

  8. A strategy for quantum algorithm design assisted by machine learning

    NASA Astrophysics Data System (ADS)

    Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung

    2014-07-01

    We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

  9. MODIS Science Algorithms and Data Systems Lessons Learned

    NASA Technical Reports Server (NTRS)

    Wolfe, Robert E.; Ridgway, Bill L.; Patt, Fred S.; Masuoka, Edward J.

    2009-01-01

    For almost 10 years, standard global products from NASA's Earth Observing System s (EOS) two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors are being used world-wide for earth science research and applications. This paper discusses the lessons learned in developing the science algorithms and the data systems needed to produce these high quality data products for the earth sciences community. Strong science team leadership and communication, an evolvable and scalable data system, and central coordination of QA and validation activities enabled the data system to grow by two orders of magnitude from the initial at-launch system to the current system able to reprocess data from both the Terra and Aqua missions in less than a year. Many of the lessons learned from MODIS are already being applied to follow-on missions.

  10. GreedEx: A Visualization Tool for Experimentation and Discovery Learning of Greedy Algorithms

    ERIC Educational Resources Information Center

    Velazquez-Iturbide, J. A.; Debdi, O.; Esteban-Sanchez, N.; Pizarro, C.

    2013-01-01

    Several years ago we presented an experimental, discovery-learning approach to the active learning of greedy algorithms. This paper presents GreedEx, a visualization tool developed to support this didactic method. The paper states the design goals of GreedEx, makes explicit the major design decisions adopted, and describes its main characteristics…

  11. Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences

    ERIC Educational Resources Information Center

    Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

    2015-01-01

    This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…

  12. Learning sorting algorithms through visualization construction

    NASA Astrophysics Data System (ADS)

    Cetin, Ibrahim; Andrews-Larson, Christine

    2016-01-01

    Recent increased interest in computational thinking poses an important question to researchers: What are the best ways to teach fundamental computing concepts to students? Visualization is suggested as one way of supporting student learning. This mixed-method study aimed to (i) examine the effect of instruction in which students constructed visualizations on students' programming achievement and students' attitudes toward computer programming, and (ii) explore how this kind of instruction supports students' learning according to their self-reported experiences in the course. The study was conducted with 58 pre-service teachers who were enrolled in their second programming class. They expect to teach information technology and computing-related courses at the primary and secondary levels. An embedded experimental model was utilized as a research design. Students in the experimental group were given instruction that required students to construct visualizations related to sorting, whereas students in the control group viewed pre-made visualizations. After the instructional intervention, eight students from each group were selected for semi-structured interviews. The results showed that the intervention based on visualization construction resulted in significantly better acquisition of sorting concepts. However, there was no significant difference between the groups with respect to students' attitudes toward computer programming. Qualitative data analysis indicated that students in the experimental group constructed necessary abstractions through their engagement in visualization construction activities. The authors of this study argue that the students' active engagement in the visualization construction activities explains only one side of students' success. The other side can be explained through the instructional approach, constructionism in this case, used to design instruction. The conclusions and implications of this study can be used by researchers and

  13. Geological Mapping Using Machine Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Harvey, A. S.; Fotopoulos, G.

    2016-06-01

    Remotely sensed spectral imagery, geophysical (magnetic and gravity), and geodetic (elevation) data are useful in a variety of Earth science applications such as environmental monitoring and mineral exploration. Using these data with Machine Learning Algorithms (MLA), which are widely used in image analysis and statistical pattern recognition applications, may enhance preliminary geological mapping and interpretation. This approach contributes towards a rapid and objective means of geological mapping in contrast to conventional field expedition techniques. In this study, four supervised MLAs (naïve Bayes, k-nearest neighbour, random forest, and support vector machines) are compared in order to assess their performance for correctly identifying geological rocktypes in an area with complete ground validation information. Geological maps of the Sudbury region are used for calibration and validation. Percent of correct classifications was used as indicators of performance. Results show that random forest is the best approach. As expected, MLA performance improves with more calibration clusters, i.e. a more uniform distribution of calibration data over the study region. Performance is generally low, though geological trends that correspond to a ground validation map are visualized. Low performance may be the result of poor spectral images of bare rock which can be covered by vegetation or water. The distribution of calibration clusters and MLA input parameters affect the performance of the MLAs. Generally, performance improves with more uniform sampling, though this increases required computational effort and time. With the achievable performance levels in this study, the technique is useful in identifying regions of interest and identifying general rocktype trends. In particular, phase I geological site investigations will benefit from this approach and lead to the selection of sites for advanced surveys.

  14. On stochastic approximation algorithms for classes of PAC learning problems

    SciTech Connect

    Rao, N.S.V.; Uppuluri, V.R.R.; Oblow, E.M.

    1994-03-01

    The classical stochastic approximation methods are shown to yield algorithms to solve several formulations of the PAC learning problem defined on the domain [o,1]{sup d}. Under some assumptions on different ability of the probability measure functions, simple algorithms to solve some PAC learning problems are proposed based on networks of non-polynomial units (e.g. artificial neural networks). Conditions on the sizes of these samples required to ensure the error bounds are derived using martingale inequalities.

  15. Protein sequence classification with improved extreme learning machine algorithms.

    PubMed

    Cao, Jiuwen; Xiong, Lianglin

    2014-01-01

    Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

  16. PAC learning algorithms for functions approximated by feedforward networks

    SciTech Connect

    Rao, N.S.V.; Protopopescu, V.

    1996-06-01

    The authors present a class of efficient algorithms for PAC learning continuous functions and regressions that are approximated by feedforward networks. The algorithms are applicable to networks with unknown weights located only in the output layer and are obtained by utilizing the potential function methods of Aizerman et al. Conditions relating the sample sizes to the error bounds are derived using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  17. Learning algorithm that gives the Bayes generalization limit for perceptrons

    NASA Astrophysics Data System (ADS)

    Kinouchi, Osame; Caticha, Nestor

    1996-07-01

    A variational approach to the study of learning a linearly separable rule by a single-layer perceptron leads to a gradient descent learning algorithm with exactly the same generalization ability as the Bayes limit calculated by Opper and Haussler [

    Phys. Rev. Lett. 66, 2677 (1991)
    ]. This is done by finding, through the Gardner-Derrida replica method, the student-teacher overlap R as a functional of the algorithm cost function and maximizing this functional. The resulting cost function is closely related to the optimal cost function derived for on-line learning.

  18. A meta-learning system based on genetic algorithms

    NASA Astrophysics Data System (ADS)

    Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

    2004-04-01

    The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

  19. Active inference and learning.

    PubMed

    Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O'Doherty, John; Pezzulo, Giovanni

    2016-09-01

    This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity.

  20. Learning factorizations in estimation of distribution algorithms using affinity propagation.

    PubMed

    Santana, Roberto; Larrañaga, Pedro; Lozano, José A

    2010-01-01

    Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and nonbinary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.

  1. Finite-sample based learning algorithms for feedforward networks

    SciTech Connect

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.

    1995-04-01

    We discuss two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by FeedForward Networks (FFN). The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can also be directly applied to concept learning problems. A main distinguishing feature of the this work is that the sample sizes are based on explicit algorithms rather than information-based methods.

  2. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

    PubMed Central

    Zwartjes, Ardjan; Havinga, Paul J. M.; Smit, Gerard J. M.; Hurink, Johann L.

    2016-01-01

    In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution. PMID:27706071

  3. Implementing a self-structuring data learning algorithm

    NASA Astrophysics Data System (ADS)

    Graham, James; Carson, Daniel; Ternovskiy, Igor

    2016-05-01

    In this paper, we elaborate on what we did to implement our self-structuring data learning algorithm. To recap, we are working to develop a data learning algorithm that will eventually be capable of goal driven pattern learning and extrapolation of more complex patterns from less complex ones. At this point we have developed a conceptual framework for the algorithm, but have yet to discuss our actual implementation and the consideration and shortcuts we needed to take to create said implementation. We will elaborate on our initial setup of the algorithm and the scenarios we used to test our early stage algorithm. While we want this to be a general algorithm, it is necessary to start with a simple scenario or two to provide a viable development and testing environment. To that end, our discussion will be geared toward what we include in our initial implementation and why, as well as what concerns we may have. In the future, we expect to be able to apply our algorithm to a more general approach, but to do so within a reasonable time, we needed to pick a place to start.

  4. Any Two Learning Algorithms Are (Almost) Exactly Identical

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.

    2000-01-01

    This paper shows that if one is provided with a loss function, it can be used in a natural way to specify a distance measure quantifying the similarity of any two supervised learning algorithms, even non-parametric algorithms. Intuitively, this measure gives the fraction of targets and training sets for which the expected performance of the two algorithms differs significantly. Bounds on the value of this distance are calculated for the case of binary outputs and 0-1 loss, indicating that any two learning algorithms are almost exactly identical for such scenarios. As an example, for any two algorithms A and B, even for small input spaces and training sets, for less than 2e(-50) of all targets will the difference between A's and B's generalization performance of exceed 1%. In particular, this is true if B is bagging applied to A, or boosting applied to A. These bounds can be viewed alternatively as telling us, for example, that the simple English phrase 'I expect that algorithm A will generalize from the training set with an accuracy of at least 75% on the rest of the target' conveys 20,000 bytes of information concerning the target. The paper ends by discussing some of the subtleties of extending the distance measure to give a full (non-parametric) differential geometry of the manifold of learning algorithms.

  5. Patch Based Multiple Instance Learning Algorithm for Object Tracking

    PubMed Central

    2017-01-01

    To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers' parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking. PMID:28321248

  6. Patch Based Multiple Instance Learning Algorithm for Object Tracking.

    PubMed

    Wang, Zhenjie; Wang, Lijia; Zhang, Hua

    2017-01-01

    To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers' parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking.

  7. Learning Algorithms for the Multilayer Perceptron

    DTIC Science & Technology

    1988-10-28

    tested , improper a priori estimation of Q results at worst in the training performance characteristic of the steepest descent algorithm. 23 300...1987. 2. F. Rosenblatt, Principles of Neurodynamics , Spartan Books, 1ew York, 1959. 3. M. Minsky, S. Papert, Perceptrons: An Introduction to

  8. Learning algorithms for feedforward networks based on finite samples

    SciTech Connect

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.

    1994-09-01

    Two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by feedforward networks, are discussed. The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  9. Learning algorithm of environmental recognition in driving vehicle

    SciTech Connect

    Qiao, L.; Sato, M.; Takeda, H.

    1995-06-01

    We consider the problem of recognizing driving environments of a vehicle by using the information obtained from some sensors of the vehicle. Previously, we presented recognition algorithms based on a usual method of pattern matching by use of distance on a vector space and fuzzy reasoning. These algorithms can not be applied to meet the demands of nonstandard drivers and changes of vehicle properties, because the standard pattern or membership function for the pattern matching is always fixed. Then to cover such weakness we presented adaptive recognition algorithms with adaptive change of the standard pattern and membership function. In this work, we put forward a fuzzy supervisor in the learning process. Also we presented an algorithm into which a new learning method is introduced to improve the performance of the previous ones and to meet the above demands. 18 refs.

  10. Optimization of circuits using a constructive learning algorithm

    SciTech Connect

    Beiu, V.

    1997-05-01

    The paper presents an application of a constructive learning algorithm to optimization of circuits. For a given Boolean function f. a fresh constructive learning algorithm builds circuits belonging to the smallest F{sub n,m} class of functions (n inputs and having m groups of ones in their truth table). The constructive proofs, which show how arbitrary Boolean functions can be implemented by this algorithm, are shortly enumerated An interesting aspect is that the algorithm can be used for generating both classical Boolean circuits and threshold gate circuits (i.e. analogue inputs and digital outputs), or a mixture of them, thus taking advantage of mixed analogue/digital technologies. One illustrative example is detailed The size and the area of the different circuits are compared (special cost functions can be used to closer estimate the area and the delay of VLSI implementations). Conclusions and further directions of research are ending the paper.

  11. Genetic-based EM algorithm for learning Gaussian mixture models.

    PubMed

    Pernkopf, Franz; Bouchaffra, Djamel

    2005-08-01

    We propose a genetic-based expectation-maximization (GA-EM) algorithm for learning Gaussian mixture models from multivariate data. This algorithm is capable of selecting the number of components of the model using the minimum description length (MDL) criterion. Our approach benefits from the properties of Genetic algorithms (GA) and the EM algorithm by combination of both into a single procedure. The population-based stochastic search of the GA explores the search space more thoroughly than the EM method. Therefore, our algorithm enables escaping from local optimal solutions since the algorithm becomes less sensitive to its initialization. The GA-EM algorithm is elitist which maintains the monotonic convergence property of the EM algorithm. The experiments on simulated and real data show that the GA-EM outperforms the EM method since: 1) We have obtained a better MDL score while using exactly the same termination condition for both algorithms. 2) Our approach identifies the number of components which were used to generate the underlying data more often than the EM algorithm.

  12. Active Learning and the LRC.

    ERIC Educational Resources Information Center

    Ducote, Richard L.

    1990-01-01

    Describes Collin County Community College's commitment to an active/experiential learning philosophy and the role of the college's learning resources center (LRC) in promoting learner-centered education and lab experiences throughout the curriculum. Discusses the LRC's Alternative Learning Center, which uses computers and other technology to…

  13. TS: a test-split algorithm for inductive learning

    NASA Astrophysics Data System (ADS)

    Wu, Xindong

    1993-09-01

    This paper presents a new attribute-based learning algorithm, TS. Different from ID3, AQ11, and HCV in strategies, this algorithm operates in cycles of test and split. It uses those attribute values which occur only in positives but not in negatives to straightforwardly discriminate positives against negatives and chooses the attributes with least number of different values to split example sets. TS is natural, easy to implement, and low-order polynomial in time complexity.

  14. Gradient Learning Algorithms for Ontology Computing

    PubMed Central

    Gao, Wei; Zhu, Linli

    2014-01-01

    The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting. PMID:25530752

  15. Manifold learning based registration algorithms applied to multimodal images.

    PubMed

    Azampour, Mohammad Farid; Ghaffari, Aboozar; Hamidinekoo, Azam; Fatemizadeh, Emad

    2014-01-01

    Manifold learning algorithms are proposed to be used in image processing based on their ability in preserving data structures while reducing the dimension and the exposure of data structure in lower dimension. Multi-modal images have the same structure and can be registered together as monomodal images if only structural information is shown. As a result, manifold learning is able to transform multi-modal images to mono-modal ones and subsequently do the registration using mono-modal methods. Based on this application, in this paper novel similarity measures are proposed for multi-modal images in which Laplacian eigenmaps are employed as manifold learning algorithm and are tested against rigid registration of PET/MR images. Results show the feasibility of using manifold learning as a way of calculating the similarity between multimodal images.

  16. Learning Sorting Algorithms through Visualization Construction

    ERIC Educational Resources Information Center

    Cetin, Ibrahim; Andrews-Larson, Christine

    2016-01-01

    Recent increased interest in computational thinking poses an important question to researchers: What are the best ways to teach fundamental computing concepts to students? Visualization is suggested as one way of supporting student learning. This mixed-method study aimed to (i) examine the effect of instruction in which students constructed…

  17. Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

    DTIC Science & Technology

    2014-03-27

    Conference on, 1–10. IEEE, 2011. [9] Cheung, S., B . Dutertre, M. Fong, U. Lindqvist, K. Skinner , and A. Valdes. “Using model-based intrusion detection for... BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING

  18. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

    PubMed

    Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S

    2017-02-13

    Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

  19. Optimization of deep learning algorithms for object classification

    NASA Astrophysics Data System (ADS)

    Horváth, András.

    2017-02-01

    Deep learning is currently the state of the art algorithm for image classification. The complexity of these feedforward neural networks have overcome a critical point, resulting algorithmic breakthroughs in various fields. On the other hand their complexity makes them executable in tasks, where High-throughput computing powers are available. The optimization of these networks -considering computational complexity and applicability on embedded systems- has not yet been studied and investigated in details. In this paper I show some examples how this algorithms can be optimized and accelerated on embedded systems.

  20. A methodology for constructing fuzzy algorithms for learning vector quantization.

    PubMed

    Karayiannis, N B

    1997-01-01

    This paper presents a general methodology for the development of fuzzy algorithms for learning vector quantization (FALVQ). The design of specific FALVQ algorithms according to existing approaches reduces to the selection of the membership function assigned to the weight vectors of an LVQ competitive neural network, which represent the prototypes. The development of a broad variety of FALVQ algorithms can be accomplished by selecting the form of the interference function that determines the effect of the nonwinning prototypes on the attraction between the winning prototype and the input of the network. The proposed methodology provides the basis for extending the existing FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms. This paper also introduces two quantitative measures which establish a relationship between the formulation that led to FALVQ algorithms and the competition between the prototypes during the learning process. The proposed algorithms and competition measures are tested and evaluated using the IRIS data set. The significance of the proposed competition measure is illustrated using FALVQ algorithms to perform segmentation of magnetic resonance images of the brain.

  1. Using animation to help students learn computer algorithms.

    PubMed

    Catrambone, Richard; Seay, A Fleming

    2002-01-01

    This paper compares the effects of graphical study aids and animation on the problem-solving performance of students learning computer algorithms. Prior research has found inconsistent effects of animation on learning, and we believe this is partly attributable to animations not being designed to convey key information to learners. We performed an instructional analysis of the to-be-learned algorithms and designed the teaching materials based on that analysis. Participants studied stronger or weaker text-based information about the algorithm, and then some participants additionally studied still frames or an animation. Across 2 studies, learners who studied materials based on the instructional analysis tended to outperform other participants on both near and far transfer tasks. Animation also aided performance, particularly for participants who initially read the weaker text. These results suggest that animation might be added to curricula as a way of improving learning without needing revisions of existing texts and materials. Actual or potential applications of this research include the development of animations for learning complex systems as well as guidelines for determining when animations can aid learning.

  2. Student Perceptions of Active Learning

    ERIC Educational Resources Information Center

    Lumpkin, Angela; Achen, Rebecca M.; Dodd, Regan K.

    2015-01-01

    A paradigm shift from lecture-based courses to interactive classes punctuated with engaging, student-centered learning activities has begun to characterize the work of some teachers in higher education. Convinced through the literature of the values of using active learning strategies, we assessed through an action research project in five college…

  3. In Defense of Active Learning

    ERIC Educational Resources Information Center

    Pica, Rae

    2008-01-01

    Effective early childhood teachers use what they know about and have observed in young children to design programs to meet children's developmental needs. Play and active learning are key tools to address those needs and facilitate children's early education. In this article, the author discusses the benefits of active learning in the education of…

  4. Cascade Error Projection: A Learning Algorithm for Hardware Implementation

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A.; Daud, Taher

    1996-01-01

    In this paper, we workout a detailed mathematical analysis for a new learning algorithm termed Cascade Error Projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters. Furthermore, CEP learning algorithm is operated only on one layer, whereas the other set of weights can be calculated deterministically. In association with the dynamical stepsize change concept to convert the weight update from infinite space into a finite space, the relation between the current stepsize and the previous energy level is also given and the estimation procedure for optimal stepsize is used for validation of our proposed technique. The weight values of zero are used for starting the learning for every layer, and a single hidden unit is applied instead of using a pool of candidate hidden units similar to cascade correlation scheme. Therefore, simplicity in hardware implementation is also obtained. Furthermore, this analysis allows us to select from other methods (such as the conjugate gradient descent or the Newton's second order) one of which will be a good candidate for the learning technique. The choice of learning technique depends on the constraints of the problem (e.g., speed, performance, and hardware implementation); one technique may be more suitable than others. Moreover, for a discrete weight space, the theoretical analysis presents the capability of learning with limited weight quantization. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.

  5. Teachers' Use of a Verbally Governed Algorithm and Student Learning

    ERIC Educational Resources Information Center

    Keohane, Dolleen-Day; Greer, R. Douglas

    2005-01-01

    The effects of instructing teachers in the use of a verbally governed algorithm to solve students' learning problems were measured. The teachers were taught to analyze students' responses to instruction using a strategic protocol, which included a series of verbally governed questions. The study was designed to determine whether the instructional…

  6. Managing and learning with multiple models: Objectives and optimization algorithms

    USGS Publications Warehouse

    Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

    2011-01-01

    The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

  7. LAHS: A novel harmony search algorithm based on learning automata

    NASA Astrophysics Data System (ADS)

    Enayatifar, Rasul; Yousefi, Moslem; Abdullah, Abdul Hanan; Darus, Amer Nordin

    2013-12-01

    This study presents a learning automata-based harmony search (LAHS) for unconstrained optimization of continuous problems. The harmony search (HS) algorithm performance strongly depends on the fine tuning of its parameters, including the harmony consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth (bw). Inspired by the spur-in-time responses in the musical improvisation process, learning capabilities are employed in the HS to select these parameters based on spontaneous reactions. An extensive numerical investigation is conducted on several well-known test functions, and the results are compared with the HS algorithm and its prominent variants, including the improved harmony search (IHS), global-best harmony search (GHS) and self-adaptive global-best harmony search (SGHS). The numerical results indicate that the LAHS is more efficient in finding optimum solutions and outperforms the existing HS algorithm variants.

  8. Using Learning Styles Inventories To Promote Active Learning.

    ERIC Educational Resources Information Center

    Fritz, Margaret

    2002-01-01

    Defines active learning as students actively involved in the learning process. Suggests that to learn actively, students need to know their learning styles and engage with the subject matter. Concludes that students who know their learning styles and are allowed to choose time management methods, note-taking systems, textbook marking methods and…

  9. Face detection based on multiple kernel learning algorithm

    NASA Astrophysics Data System (ADS)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun

    2016-09-01

    Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or not, and also its location, size. It can be seen as a binary classification problem, which can be well solved by support vector machine (SVM). Though SVM has strong model generalization ability, it has some limitations, which will be deeply analyzed in the paper. To access them, we study the principle and characteristics of the Multiple Kernel Learning (MKL) and propose a MKL-based face detection algorithm. In the paper, we describe the proposed algorithm in the interdisciplinary research perspective of machine learning and image processing. After analyzing the limitation of describing a face with a single feature, we apply several ones. To fuse them well, we try different kernel functions on different feature. By MKL method, the weight of each single function is determined. Thus, we obtain the face detection model, which is the kernel of the proposed method. Experiments on the public data set and real life face images are performed. We compare the performance of the proposed algorithm with the single kernel-single feature based algorithm and multiple kernels-single feature based algorithm. The effectiveness of the proposed algorithm is illustrated. Keywords: face detection, feature fusion, SVM, MKL

  10. Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms.

    ERIC Educational Resources Information Center

    Chen, Hsinchun

    1995-01-01

    Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…

  11. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    SciTech Connect

    Bornholdt, S.; Graudenz, D.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  12. Denoising of gravitational wave signals via dictionary learning algorithms

    NASA Astrophysics Data System (ADS)

    Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

    2016-12-01

    Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

  13. Learning as a Subversive Activity

    ERIC Educational Resources Information Center

    Hatch, J. Amos

    2007-01-01

    "Learning as a subversive activity" is about working with public school students to debunk the shallow conception that achievement equals learning. That means exposing the power relations that keep in place such a narrow definition of what counts and exploring the implications of those powerful forces for students' lives and for society at large.…

  14. Computer aided lung cancer diagnosis with deep learning algorithms

    NASA Astrophysics Data System (ADS)

    Sun, Wenqing; Zheng, Bin; Qian, Wei

    2016-03-01

    Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

  15. Developing a Learning Algorithm-Generated Empirical Relaxer

    SciTech Connect

    Mitchell, Wayne; Kallman, Josh; Toreja, Allen; Gallagher, Brian; Jiang, Ming; Laney, Dan

    2016-03-30

    One of the main difficulties when running Arbitrary Lagrangian-Eulerian (ALE) simulations is determining how much to relax the mesh during the Eulerian step. This determination is currently made by the user on a simulation-by-simulation basis. We present a Learning Algorithm-Generated Empirical Relaxer (LAGER) which uses a regressive random forest algorithm to automate this decision process. We also demonstrate that LAGER successfully relaxes a variety of test problems, maintains simulation accuracy, and has the potential to significantly decrease both the person-hours and computational hours needed to run a successful ALE simulation.

  16. Astrophysical Parameter Estimation for Gaia using Machine Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Tiede, C.; Smith, K.; Bailer-Jones, C. A. L.

    2008-08-01

    Gaia is the next astrometric mission from ESA and will measure objects up to a magnitude of about G=20. Depending on the kind of object (which will be determined automatically because Gaia does not hold an input catalogue), the specific astrophysical parameters will be estimated. The General Stellar Parametrizer (GSP-phot) estimates the astrophysical parameters based on low-dispersion spectra and parallax information for single stars. We show the results of machine learning algorithms trained on simulated data and further developments of the core algorithms which improve the accuracy of the estimated astrophysical parameters.

  17. Formalizing Neurath's ship: Approximate algorithms for online causal learning.

    PubMed

    Bramley, Neil R; Dayan, Peter; Griffiths, Thomas L; Lagnado, David A

    2017-04-01

    Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record

  18. [A new algorithm for NIR modeling based on manifold learning].

    PubMed

    Hong, Ming-Jian; Wen, Zhi-Yu; Zhang, Xiao-Hong; Wen, Quan

    2009-07-01

    Manifold learning is a new kind of algorithm originating from the field of machine learning to find the intrinsic dimensionality of numerous and complex data and to extract most important information from the raw data to develop a regression or classification model. The basic assumption of the manifold learning is that the high-dimensional data measured from the same object using some devices must reside on a manifold with much lower dimensions determined by a few properties of the object. While NIR spectra are characterized by their high dimensions and complicated band assignment, the authors may assume that the NIR spectra of the same kind of substances with different chemical concentrations should reside on a manifold with much lower dimensions determined by the concentrations, according to the above assumption. As one of the best known algorithms of manifold learning, locally linear embedding (LLE) further assumes that the underlying manifold is locally linear. So, every data point in the manifold should be a linear combination of its neighbors. Based on the above assumptions, the present paper proposes a new algorithm named least square locally weighted regression (LS-LWR), which is a kind of LWR with weights determined by the least squares instead of a predefined function. Then, the NIR spectra of glucose solutions with various concentrations are measured using a NIR spectrometer and LS-LWR is verified by predicting the concentrations of glucose solutions quantitatively. Compared with the existing algorithms such as principal component regression (PCR) and partial least squares regression (PLSR), the LS-LWR has better predictability measured by the standard error of prediction (SEP) and generates an elegant model with good stability and efficiency.

  19. Tomography reconstruction using the Learn and Apply algorithm

    NASA Astrophysics Data System (ADS)

    Vidal, F.; Gendron, E.; Brangier, M.; Sevin, A.; Rousset, G.; Hubert, Z.

    In the framework of the MOAO demonstrator CANARY, we developped a new concept of tomography algorithm that allows to measure the tomographic reconstructor directly on-sky, using or not, a priori from the turbulence profile. This simple algorithm, working in open-loop, uses the measured covariance of slopes between all the wavefront sensors (WFS) to deduce the geometric and atmospheric parameters that are used to compute the tomographic reconstructor. This method called “Learn and Apply” (L&A) has also the advantage to measure and take into account all the misalignments between the WFSs in order to calibrate any MOAO instrument. We present the main principle of the algorithm and the last experimental results performed in MOAO scheme at the SESAME bench.

  20. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  1. Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

    PubMed

    Walter, Florian; Röhrbein, Florian; Knoll, Alois

    2015-12-01

    The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks.

  2. The No-Prop algorithm: a new learning algorithm for multilayer neural networks.

    PubMed

    Widrow, Bernard; Greenblatt, Aaron; Kim, Youngsik; Park, Dookun

    2013-01-01

    A new learning algorithm for multilayer neural networks that we have named No-Propagation (No-Prop) is hereby introduced. With this algorithm, the weights of the hidden-layer neurons are set and fixed with random values. Only the weights of the output-layer neurons are trained, using steepest descent to minimize mean square error, with the LMS algorithm of Widrow and Hoff. The purpose of introducing nonlinearity with the hidden layers is examined from the point of view of Least Mean Square Error Capacity (LMS Capacity), which is defined as the maximum number of distinct patterns that can be trained into the network with zero error. This is shown to be equal to the number of weights of each of the output-layer neurons. The No-Prop algorithm and the Back-Prop algorithm are compared. Our experience with No-Prop is limited, but from the several examples presented here, it seems that the performance regarding training and generalization of both algorithms is essentially the same when the number of training patterns is less than or equal to LMS Capacity. When the number of training patterns exceeds Capacity, Back-Prop is generally the better performer. But equivalent performance can be obtained with No-Prop by increasing the network Capacity by increasing the number of neurons in the hidden layer that drives the output layer. The No-Prop algorithm is much simpler and easier to implement than Back-Prop. Also, it converges much faster. It is too early to definitively say where to use one or the other of these algorithms. This is still a work in progress.

  3. An augmented Lagrangian multi-scale dictionary learning algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Qiegen; Luo, Jianhua; Wang, Shanshan; Xiao, Moyan; Ye, Meng

    2011-12-01

    Learning overcomplete dictionaries for sparse signal representation has become a hot topic fascinated by many researchers in the recent years, while most of the existing approaches have a serious problem that they always lead to local minima. In this article, we present a novel augmented Lagrangian multi-scale dictionary learning algorithm (ALM-DL), which is achieved by first recasting the constrained dictionary learning problem into an AL scheme, and then updating the dictionary after each inner iteration of the scheme during which majorization-minimization technique is employed for solving the inner subproblem. Refining the dictionary from low scale to high makes the proposed method less dependent on the initial dictionary hence avoiding local optima. Numerical tests for synthetic data and denoising applications on real images demonstrate the superior performance of the proposed approach.

  4. Finite Element Learning Modules as Active Learning Tools

    ERIC Educational Resources Information Center

    Brown, Ashland O.; Jensen, Daniel; Rencis, Joseph; Wood, Kristin; Wood, John; White, Christina; Raaberg, Kristen Kaufman; Coffman, Josh

    2012-01-01

    The purpose of active learning is to solicit participation by students beyond the passive mode of traditional classroom lectures. Reading, writing, participating in discussions, hands-on activities, engaging in active problem solving, and collaborative learning can all be involved. The skills acquired during active learning tend to go above and…

  5. [Field Learning Activities].

    ERIC Educational Resources Information Center

    Nolde Forest Environmental Education Center, Reading, PA.

    Seventy field activities, pertinent to outdoor, environmental studies, are described in this compilation. Designed for elementary and junior high school students, the activities cover many discipline areas--science, social studies, language arts, health, history, mathematics, and art--and many are multidisciplinary in use. Topics range from soil…

  6. Learning Activities for Toddlers.

    ERIC Educational Resources Information Center

    Texas Child Care, 1996

    1996-01-01

    Suggests activities to help toddlers develop skills in the four important areas of self-help, creativity, world mastery, and coordination. Activities include hand washing, button practice, painting, movement and music, bubble making, creation of a nature mural, and a shoe print trail. (TJQ)

  7. A numeric comparison of variable selection algorithms for supervised learning

    NASA Astrophysics Data System (ADS)

    Palombo, G.; Narsky, I.

    2009-12-01

    Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( http://sourceforge.net/projects/statpatrec/). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ("Add N Remove R") implemented in SPR.

  8. Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

    NASA Astrophysics Data System (ADS)

    Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

    2017-02-01

    We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

  9. The Design and Analysis of Efficient Learning Algorithms

    DTIC Science & Technology

    1991-01-01

    example, a car buzzer buzzes if the key is in the ignition and the door is open, or if the motor is on and the seat belt is unfastened. Let K, D, M... belt is fastened. Then the buzzer buzzes if and only if the Boolean formula (K AND D) OR (M AND NOT(S)) evaluates to true. Here, AND, OR and NOT are...early childhood development. Wilson [87] studies so-called genetic algorithms for learning by "animats" in unfamiliar environments. Kuipers and Byun

  10. Business Communication through Active Learning.

    ERIC Educational Resources Information Center

    Goff-Kfouri, Carol Ann

    Research has shown that although university instructors of English as a Second Language are aware of the benefits that active learning can bring the student, teacher-centered, traditional lecture method classes are still the norm. Resistance to change is due in part to large class sizes, limited instruction hours, and the perception that proactive…

  11. Adapting Active Learning in Ethiopia

    ERIC Educational Resources Information Center

    Casale, Carolyn Frances

    2010-01-01

    Ethiopia is a developing country that has invested extensively in expanding its educational opportunities. In this expansion, there has been a drastic restructuring of its system of preparing teachers and teacher educators. Often, improving teacher quality is dependent on professional development that diversifies pedagogy (active learning). This…

  12. Oral Hygiene. Learning Activity Package.

    ERIC Educational Resources Information Center

    Hime, Kirsten

    This learning activity package on oral hygiene is one of a series of 12 titles developed for use in health occupations education programs. Materials in the package include objectives, a list of materials needed, a list of definitions, information sheets, reviews (self evaluations) of portions of the content, and answers to reviews. These topics…

  13. Learning Style Differences in the Perceived Effectiveness of Learning Activities

    ERIC Educational Resources Information Center

    Karns, Gary L.

    2006-01-01

    The learning style individual difference factor has long been a basis for understanding student preferences for various learning activities. Marketing educators have been advised to heavily invest in tailoring course design based on the learning style groups in their classes. A further exploration of the effects of learning style differences on…

  14. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    NASA Astrophysics Data System (ADS)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  15. Stimulating Deep Learning Using Active Learning Techniques

    ERIC Educational Resources Information Center

    Yew, Tee Meng; Dawood, Fauziah K. P.; a/p S. Narayansany, Kannaki; a/p Palaniappa Manickam, M. Kamala; Jen, Leong Siok; Hoay, Kuan Chin

    2016-01-01

    When students and teachers behave in ways that reinforce learning as a spectator sport, the result can often be a classroom and overall learning environment that is mostly limited to transmission of information and rote learning rather than deep approaches towards meaningful construction and application of knowledge. A group of college instructors…

  16. Mixed learning algorithms and features ensemble in hepatotoxicity prediction

    NASA Astrophysics Data System (ADS)

    Liew, Chin Yee; Lim, Yen Ching; Yap, Chun Wei

    2011-09-01

    Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.

  17. Reinforcement learning algorithms for robotic navigation in dynamic environments.

    PubMed

    Yen, Gary G; Hickey, Travis W

    2004-04-01

    The purpose of this study was to examine improvements to reinforcement learning (RL) algorithms in order to successfully interact within dynamic environments. The scope of the research was that of RL algorithms as applied to robotic navigation. Proposed improvements include: addition of a forgetting mechanism, use of feature based state inputs, and hierarchical structuring of an RL agent. Simulations were performed to evaluate the individual merits and flaws of each proposal, to compare proposed methods to prior established methods, and to compare proposed methods to theoretically optimal solutions. Incorporation of a forgetting mechanism did considerably improve the learning times of RL agents in a dynamic environment. However, direct implementation of a feature-based RL agent did not result in any performance enhancements, as pure feature-based navigation results in a lack of positional awareness, and the inability of the agent to determine the location of the goal state. Inclusion of a hierarchical structure in an RL agent resulted in significantly improved performance, specifically when one layer of the hierarchy included a feature-based agent for obstacle avoidance, and a standard RL agent for global navigation. In summary, the inclusion of a forgetting mechanism, and the use of a hierarchically structured RL agent offer substantially increased performance when compared to traditional RL agents navigating in a dynamic environment.

  18. Connecting Family Learning and Active Citizenship

    ERIC Educational Resources Information Center

    Flanagan, Mary

    2009-01-01

    In Ireland family learning and active citizenship has not been linked together until 2006. It was while the Clare Family Learning Project was involved in a family learning EU learning network project, that a suggestion to create a new partnership project linking both areas was made and FACE IT! was born (Families and Active Citizenship…

  19. Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

    NASA Astrophysics Data System (ADS)

    Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M.

    2017-02-01

    We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.

  20. Pediatric Brain Extraction Using Learning-based Meta-algorithm

    PubMed Central

    Shi, Feng; Wang, Li; Dai, Yakang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

    2012-01-01

    Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2 months), infant (1–2 years), and child (5–18 years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range. PMID:22634859

  1. Holographic implementation of a learning machine based on a multicategory perceptron algorithm.

    PubMed

    Paek, E G; Wullert Ii, J R; Patel, J S

    1989-12-01

    An optical learning machine that has multicategory classification capability is demonstrated. The system exactly implements the single-layer perceptron algorithm and is fully parallel and analog. Experimental results on the learning by examples obtained from the system are described.

  2. Active Learning: Historical and Contemporary Perspectives.

    ERIC Educational Resources Information Center

    Page, Marilyn

    The purposes of the first two parts of this literature review are to clarify the concept of active learning and discuss the use and value of active learning models. In Part I, the perspectives of five historical proponents of active learning, Rousseau, Pestalozzi, Dewey, Kilpatrick, and Piaget, are discussed. The views of four contemporary…

  3. EM&AA: An Algorithm for Predicting the Course Selection by Student in e-Learning Using Data Mining Techniques

    NASA Astrophysics Data System (ADS)

    Aher, Sunita B.

    2014-01-01

    Recommendation systems have been widely used in internet activities whose aim is to present the important and useful information to the user with little effort. Course Recommendation System is system which recommends to students the best combination of courses in engineering education system e.g. if student is interested in course like system programming then he would like to learn the course entitled compiler construction. The algorithm with combination of two data mining algorithm i.e. combination of Expectation Maximization Clustering and Apriori Association Rule Algorithm have been developed. The result of this developed algorithm is compared with Apriori Association Rule Algorithm which is an existing algorithm in open source data mining tool Weka.

  4. Active Learning through Service-Learning

    ERIC Educational Resources Information Center

    Goldberg, Lynette R.; Richburg, Cynthia McCormick; Wood, Lisa A.

    2006-01-01

    Service-learning (SL) is a relatively new pedagogical approach to facilitate student learning at the university level. In SL, students enrolled in an academic course provide a needed service to a community partner. Through guided reflection, students link classroom-based, theoretical knowledge with clinical applications. Students' active…

  5. A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.

    PubMed

    Vuković, Najdan; Miljković, Zoran

    2013-10-01

    Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems. To accommodate limitation of a single scale, this paper presents neural network with similar but yet different activation function-hyper basis function (HBF). The HBF allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The HBF is based on generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. Compared to the RBF, the HBF neuron has more parameters to optimize, but HBF neural network needs less number of HBF neurons to memorize relationship between input and output sets in order to achieve good generalization property. However, recent research results of HBF neural network performance have shown that optimal way of constructing this type of neural network is needed; this paper addresses this issue and modifies sequential learning algorithm for HBF neural network that exploits the concept of neuron's significance and allows growing and pruning of HBF neuron during learning process. Extensive experimental study shows that HBF neural network, trained with developed learning algorithm, achieves lower prediction error and more compact neural network.

  6. Developing Metacognition: A Basis for Active Learning

    ERIC Educational Resources Information Center

    Vos, Henk; de Graaff, E.

    2004-01-01

    The reasons to introduce formats of active learning in engineering (ALE) such as project work, problem-based learning, use of cases, etc. are mostly based on practical experience, and sometimes from applied research on teaching and learning. Such research shows that students learn more and different abilities than in traditional formats of…

  7. MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Albayrak, A.; Wei, J. C.; Petrenko, M.; Lary, D. J.; Leptoukh, G. G.

    2011-12-01

    Over the past decade, global aerosol observations have been conducted by space-borne sensors, airborne instruments, and ground-base network measurements. Unfortunately, quite often we encounter the differences of aerosol measurements by different well-calibrated instruments, even with a careful collocation in time and space. The differences might be rather substantial, and need to be better understood and accounted for when merging data from many sensors. The possible causes for these differences come from instrumental bias, different satellite viewing geometries, calibration issues, dynamically changing atmospheric and the surface conditions, and other "regressors", resulting in random and systematic errors in the final aerosol products. In this study, we will concentrate on the subject of removing biases and the systematic errors from MODIS (both Terra and Aqua) aerosol product, using Machine Learning algorithms. While we are assessing our regressors in our system when comparing global aerosol products, the Aerosol Robotic Network of sun-photometers (AERONET) will be used as a baseline for evaluating the MODIS aerosol products (Dark Target for land and ocean, and Deep Blue retrieval algorithms). The results of bias adjustment for MODIS Terra and Aqua are planned to be incorporated into the AeroStat Giovanni as part of the NASA ACCESS funded AeroStat project.

  8. Modeling the Swift BAT Trigger Algorithm with Machine Learning

    NASA Astrophysics Data System (ADS)

    Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori

    2016-02-01

    To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of ≳97% (≲3% error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6% (10.4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of {n}0∼ {0.48}-0.23+0.41 {{{Gpc}}}-3 {{{yr}}}-1 with power-law indices of {n}1∼ {1.7}-0.5+0.6 and {n}2∼ -{5.9}-0.1+5.7 for GRBs above and below a break point of {z}1∼ {6.8}-3.2+2.8. This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

  9. IDEAL: Images Across Domains, Experiments, Algorithms and Learning

    NASA Astrophysics Data System (ADS)

    Ushizima, Daniela M.; Bale, Hrishikesh A.; Bethel, E. Wes; Ercius, Peter; Helms, Brett A.; Krishnan, Harinarayan; Grinberg, Lea T.; Haranczyk, Maciej; Macdowell, Alastair A.; Odziomek, Katarzyna; Parkinson, Dilworth Y.; Perciano, Talita; Ritchie, Robert O.; Yang, Chao

    2016-11-01

    Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this paper, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

  10. Experiments on Supervised Learning Algorithms for Text Categorization

    NASA Technical Reports Server (NTRS)

    Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.

    2005-01-01

    Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

  11. Learning activism, acting with phronesis

    NASA Astrophysics Data System (ADS)

    Lee, Yew-Jin

    2015-12-01

    The article "Socio-political development of private school children mobilising for disadvantaged others" by Darren Hoeg, Natalie Lemelin, and Lawrence Bencze described a language-learning curriculum that drew on elements of Socioscientific issues and Science, Technology, Society and Environment. Results showed that with a number of enabling factors acting in concert, learning about and engagement in practical action for social justice and equity are possible. An alternative but highly compatible framework is now introduced—phronetic social research—as an action-oriented, wisdom-seeking research stance for the social sciences. By so doing, it is hoped that forms of phronetic social research can gain wider currency among those that promote activism as one of many valued outcomes of an education in science.

  12. Linking Mission to Learning Activities for Assurance of Learning

    ERIC Educational Resources Information Center

    Yeung, Shirley Mo-ching

    2011-01-01

    Can accreditation-related requirements and mission statements measure learning outcomes? This study focuses on triangulating accreditation-related requirements with mission statements and learning activities to learning outcomes. This topic has not been comprehensively explored in the past. After looking into the requirements of AACSB, ISO, and…

  13. Effective and efficient optics inspection approach using machine learning algorithms

    SciTech Connect

    Abdulla, G; Kegelmeyer, L; Liao, Z; Carr, W

    2010-11-02

    The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

  14. Iterative learning control algorithm for spiking behavior of neuron model

    NASA Astrophysics Data System (ADS)

    Li, Shunan; Li, Donghui; Wang, Jiang; Yu, Haitao

    2016-11-01

    Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method-closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin-Huxley (HH) model for class 1 neural excitability and Morris-Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

  15. Learning Activities for the Young Handicapped Child.

    ERIC Educational Resources Information Center

    Bailey, Don; And Others

    Presented is a collection of learning activities for the young handicapped child covering 295 individual learning objectives in six areas of development: gross motor skills, fine motor skills, social skills, self help skills, cognitive skills, and language skills. Provided for each learning activity are the teaching objective, teaching procedures,…

  16. Research on Mobile Learning Activities Applying Tablets

    ERIC Educational Resources Information Center

    Kurilovas, Eugenijus; Juskeviciene, Anita; Bireniene, Virginija

    2015-01-01

    The paper aims to present current research on mobile learning activities in Lithuania while implementing flagship EU-funded CCL project on application of tablet computers in education. In the paper, the quality of modern mobile learning activities based on learning personalisation, problem solving, collaboration, and flipped class methods is…

  17. Optimizing the Learning Order of Chinese Characters Using a Novel Topological Sort Algorithm

    PubMed Central

    Wang, Jinzhao

    2016-01-01

    We present a novel algorithm for optimizing the order in which Chinese characters are learned, one that incorporates the benefits of learning them in order of usage frequency and in order of their hierarchal structural relationships. We show that our work outperforms previously published orders and algorithms. Our algorithm is applicable to any scheduling task where nodes have intrinsic differences in importance and must be visited in topological order. PMID:27706234

  18. CME Prediction Using SDO, SoHO, and STEREO data with a Machine Learning Algorithm

    NASA Astrophysics Data System (ADS)

    Bobra, M.; Ilonidis, S.

    2015-12-01

    It is unclear whether a flaring active region will also produce a Coronal Mass Ejection (CME). Usually, active regions that produce large flares will also produce a CME, but this is not always the case. For example, the largest active region from the last 24 years, NOAA Active Region 12192 of October 2014, produced many X-class flares but not a single CME. We attempt to forecast whether an active region that produces an M- or X-class flare will also produce a CME. We do this by analyzing data from three solar observatories -- SDO, STEREO, and SoHO -- using a machine-learning algorithm. We find that the role of horizontal component of the photospheric magnetic field plays a crucial component in driving a CME, a result corroborated by Sun et al. (2015). We present the success rate of our method and the potential applications to space weather forecasts.

  19. Reinforcement learning or active inference?

    PubMed

    Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

    2009-07-29

    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  20. Reinforcement Learning or Active Inference?

    PubMed Central

    Friston, Karl J.; Daunizeau, Jean; Kiebel, Stefan J.

    2009-01-01

    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain. PMID:19641614

  1. Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data.

    PubMed

    Zumer, Johanna M; Attias, Hagai T; Sekihara, Kensuke; Nagarajan, Srikantan S

    2008-07-01

    We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886-3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.

  2. Preparing students to participate in an active learning environment.

    PubMed

    Modell, H I

    1996-06-01

    Most students have spent the majority of their school career in passive learning environments in which faculty were disseminators of information, and students were required to memorize information or use specified algorithms to "solve problems." In an active learning environment, students are encouraged to engage in the process of building and testing their own mental models from information that they are acquiring. In such a learner-centered environment, faculty become facilitators of learning, and students become active participants, engaging in a dialogue with their colleagues and with the instructor. To create a successful active learning environment, both faculty and students must make adjustments to what has been their respective "traditional" roles in the classroom. For the instructor who is committed to promoting active learning, the challenge lies in helping students understand the necessity of becoming active colleagues in learning. This process can be facilitated if the curriculum includes exercises to direct students' attention to a number of issues that impact their learning. This paper describes four such exercises designed to help students form appropriate course expectations, recognize the need for seeking clarification when communicating, recognize the role of personal experience in building mental models, and become familiar with study aids for building formal models.

  3. A Colloquial Approach: An Active Learning Technique.

    ERIC Educational Resources Information Center

    Arce, Pedro

    1994-01-01

    Addresses the problem of the effectiveness of teaching methodologies on fundamental engineering courses such as transport phenomena. Recommends the colloquial approach, an active learning strategy, to increase student involvement in the learning process. (ZWH)

  4. A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

    PubMed Central

    Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

    2016-01-01

    A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

  5. A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms.

    PubMed

    Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O

    2016-12-23

    A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems.

  6. Active Learning-Based Pedagogical Rule Extraction.

    PubMed

    Junqué de Fortuny, Enric; Martens, David

    2015-11-01

    Many of the state-of-the-art data mining techniques introduce nonlinearities in their models to cope with complex data relationships effectively. Although such techniques are consistently included among the top classification techniques in terms of predictive power, their lack of transparency renders them useless in any domain where comprehensibility is of importance. Rule-extraction algorithms remedy this by distilling comprehensible rule sets from complex models that explain how the classifications are made. This paper considers a new rule extraction technique, based on active learning. The technique generates artificial data points around training data with low confidence in the output score, after which these are labeled by the black-box model. The main novelty of the proposed method is that it uses a pedagogical approach without making any architectural assumptions of the underlying model. It can therefore be applied to any black-box technique. Furthermore, it can generate any rule format, depending on the chosen underlying rule induction technique. In a large-scale empirical study, we demonstrate the validity of our technique to extract trees and rules from artificial neural networks, support vector machines, and random forests, on 25 data sets of varying size and dimensionality. Our results show that not only do the generated rules explain the black-box models well (thereby facilitating the acceptance of such models), the proposed algorithm also performs significantly better than traditional rule induction techniques in terms of accuracy as well as fidelity.

  7. Modeling the Swift BAT Trigger Algorithm with Machine Learning

    NASA Technical Reports Server (NTRS)

    Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori

    2015-01-01

    To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.

  8. Predicting mining activity with parallel genetic algorithms

    USGS Publications Warehouse

    Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,

    2005-01-01

    We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.

  9. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    PubMed

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.

  10. Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

    NASA Astrophysics Data System (ADS)

    Singh, R.; Verma, H. K.

    2013-12-01

    This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

  11. Design of Learning Model of Logic and Algorithms Based on APOS Theory

    ERIC Educational Resources Information Center

    Hartati, Sulis Janu

    2014-01-01

    This research questions were "how do the characteristics of learning model of logic & algorithm according to APOS theory" and "whether or not these learning model can improve students learning outcomes". This research was conducted by exploration, and quantitative approach. Exploration used in constructing theory about the…

  12. Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique

    ERIC Educational Resources Information Center

    Hsu, Pi-Shan; Chang, Te-Jeng

    2011-01-01

    The objective of this research is to validate the algorithm of learning effort which is an indicator of a new real-time and non-interfering based diagnostic technique. IC3 Mentor, the adaptive e-learning platform fulfilling the requirements of intelligent tutor system, was applied to 165 university students. The learning records of the subjects…

  13. Active learning for clinical text classification: is it better than random sampling?

    PubMed Central

    Figueroa, Rosa L; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

    2012-01-01

    Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty. PMID:22707743

  14. Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

    PubMed Central

    Tian, Yuling; Zhang, Hongxian

    2016-01-01

    For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242

  15. New machine-learning algorithms for prediction of Parkinson's disease

    NASA Astrophysics Data System (ADS)

    Mandal, Indrajit; Sairam, N.

    2014-03-01

    This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

  16. Ordering and finding the best of K > 2 supervised learning algorithms.

    PubMed

    Yildiz, Olcay Taner; Alpaydin, Ethem

    2006-03-01

    Given a data set and a number of supervised learning algorithms, we would like to find the algorithm with the smallest expected error. Existing pairwise tests allow a comparison of two algorithms only; range tests and ANOVA check whether multiple algorithms have the same expected error and cannot be used for finding the smallest. We propose a methodology, the MultiTest algorithm, whereby we order supervised learning algorithms taking into account 1) the result of pairwise statistical tests on expected error (what the data tells us), and 2) our prior preferences, e.g., due to complexity. We define the problem in graph-theoretic terms and propose an algorithm to find the "best" learning algorithm in terms of these two criteria, or in the more general case, order learning algorithms in terms of their "goodness." Simulation results using five classification algorithms on 30 data sets indicate the utility of the method. Our proposed method can be generalized to regression and other loss functions by using a suitable pairwise test.

  17. Modelling Typical Online Language Learning Activity

    ERIC Educational Resources Information Center

    Montoro, Carlos; Hampel, Regine; Stickler, Ursula

    2014-01-01

    This article presents the methods and results of a four-year-long research project focusing on the language learning activity of individual learners using online tasks conducted at the University of Guanajuato (Mexico) in 2009-2013. An activity-theoretical model (Blin, 2010; Engeström, 1987) of the typical language learning activity was used to…

  18. Activities for Science: Cooperative Learning Lessons (Challenging).

    ERIC Educational Resources Information Center

    Jasmine, Grace; Jasmine, Julia

    This book is designed to help advanced elementary students learn science skills while actively engaged in cooperative activities based on the earth sciences and natural disasters. The first section explains how to make cooperative learning a part of the curriculum and includes an overview, instructions and activities to bring cooperative learning…

  19. Learning Activities of Disadvantaged Older Adults.

    ERIC Educational Resources Information Center

    Heisel, Marsel A.

    1986-01-01

    This study aimed to investigate how 132 poor, urban, elderly black persons engage in formal and informal learning activities and the relation of such activities to educational histories and current life satisfaction. Findings show that the population is involved in purposeful learning activities and is motivated to pursue educational interests.…

  20. Producing Learning Activities Packages. Instructor's Manual.

    ERIC Educational Resources Information Center

    Jobe, Holly; Cannon, Glenn

    This teachers' manual outlines the design, development, and evaluation processes for Learning Activities Packages (LAPS), including mediated learning activities. A lesson plan for the first day's instruction is provided, as well as a 20-item pre-post test. Each LAP has five components: concept, rationale, objectives, preassessment, activities, and…

  1. Topics in Computational Learning Theory and Graph Algorithms.

    ERIC Educational Resources Information Center

    Board, Raymond Acton

    This thesis addresses problems from two areas of theoretical computer science. The first area is that of computational learning theory, which is the study of the phenomenon of concept learning using formal mathematical models. The goal of computational learning theory is to investigate learning in a rigorous manner through the use of techniques…

  2. Reinforcement active learning in the vibrissae system: optimal object localization.

    PubMed

    Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

    2013-01-01

    Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment.

  3. Kinaesthetic Learning Activities and Learning about Solar Cells

    ERIC Educational Resources Information Center

    Richards, A. J.; Etkina, Eugenia

    2013-01-01

    Kinaesthetic learning activities (KLAs) can be a valuable pedagogical tool for physics instructors. They have been shown to increase engagement, encourage participation and improve learning outcomes. This paper details several KLAs developed at Rutgers University for inclusion in an instructional unit about semiconductors, p-n junctions and solar…

  4. Adult Learning Principles in Designing Learning Activities for Teacher Development

    ERIC Educational Resources Information Center

    Gravani, Maria N.

    2012-01-01

    The research reported in this paper is an investigation of the application of adult learning principles in designing learning activities for teachers' life-long development. The exploration is illustrated by qualitative data from a case study of adult educators' and adult learners' insights and experiences of a teacher development course organised…

  5. Student Activity and Learning Outcomes in a Virtual Learning Environment

    ERIC Educational Resources Information Center

    Romanov, Kalle; Nevgi, Anne

    2008-01-01

    The aim of the study was to explore the relationship between degree of participation and learning outcomes in an e-learning course on medical informatics. Overall activity in using course materials and degree of participation in the discussion forums of an online course were studied among 39 medical students. Students were able to utilise the…

  6. A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

    PubMed

    Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

  7. Distributed learning automata-based algorithm for community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

    2016-03-01

    Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

  8. Adding learning to cellular genetic algorithms for training recurrent neural networks.

    PubMed

    Ku, K W; Mak, M W; Siu, W C

    1999-01-01

    This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA's) for training recurrent neural networks (RNN's). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GA's and learning have been compared. Different hill-climbing algorithms are incorporated into the cellular GA's as learning methods. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. The RTRL algorithm has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, which is the simplest form of learning, has been implemented by considering the RNN's as feedforward networks during learning. The hybrid algorithms are used to train the RNN's to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations required for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA's has been found to be the fastest method. It is also concluded that learning should not be too extensive if the hybrid algorithm is to be benefit from learning.

  9. Faculty Adoption of Active Learning Classrooms

    ERIC Educational Resources Information Center

    Van Horne, Sam; Murniati, Cecilia Titiek

    2016-01-01

    Although post-secondary educational institutions are incorporating more active learning classrooms (ALCs) that support collaborative learning, researchers have less often examined the cultural obstacles to adoption of those environments. In this qualitative research study, we adopted the conceptual framework of activity theory to examine the…

  10. Four Variations on Drueke's Active Learning Paradigm.

    ERIC Educational Resources Information Center

    Ragains, Patrick

    1995-01-01

    A lesson structure for one-time bibliographic instruction (BI) sessions based on an active learning technique was developed. Active learning is discussed, and the "jigsaw method" is described. BI sessions presented to junior- and senior-level college students are examined, and considerations for librarians wishing to incorporate active…

  11. Active Learning in American History Class.

    ERIC Educational Resources Information Center

    Brill, Janice

    1996-01-01

    Describes the activities of a high school class that discovered the joy of history through experiential learning. Students learned traditional military tactics for their unit on the French and Indian Wars, and tried to apply them to a nearby woods. Includes similar activities for other historic periods. (MJP)

  12. An active set algorithm for treatment planning optimization.

    PubMed

    Hristov, D H; Fallone, B G

    1997-09-01

    An active set algorithm for optimization of radiation therapy dose planning by intensity modulated beams has been developed. The algorithm employs a conjugate-gradient routine for subspace minimization in order to achieve a higher rate of convergence than the widely used constrained steepest-descent method at the expense of a negligible amount of overhead calculations. The performance of the new algorithm has been compared to that of the constrained steepest-descent method for various treatment geometries and two different objectives. The active set algorithm is found to be superior to the constrained steepest descent, both in terms of its convergence properties and the residual value of the cost functions at termination. Its use can significantly accelerate the design of conformal plans with intensity modulated beams by decreasing the number of time-consuming dose calculations.

  13. Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications

    DTIC Science & Technology

    2006-03-01

    Mapreduce : Simplified data processing on large clusters . In Symposium on Operating System Design and Implementation, 2004. 6.3.2 S. C. Deerwester, S. T...Fast Nonparametric Machine Learning Algorithms for High-dimensional Massive Data and Applications Ting Liu CMU-CS-06-124 March 2006 School of...4. TITLE AND SUBTITLE Fast Nonparametric Machine Learning Algorithms for High-dimensional Massive Data and Applications 5a. CONTRACT NUMBER 5b

  14. Active Ageing, Active Learning: Policy and Provision in Hong Kong

    ERIC Educational Resources Information Center

    Tam, M.

    2011-01-01

    This paper discusses the relationship between ageing and learning, previous literature having confirmed that participation in continued learning in old age contributes to good health, satisfaction with life, independence and self-esteem. Realizing that learning is vital to active ageing, the Hong Kong government has implemented policies and…

  15. Ensemble of classifiers to improve accuracy of the CLIP4 machine-learning algorithm

    NASA Astrophysics Data System (ADS)

    Kurgan, Lukasz; Cios, Krzysztof J.

    2002-03-01

    Machine learning, one of the data mining and knowledge discovery tools, addresses automated extraction of knowledge from data, expressed in the form of production rules. The paper describes a method for improving accuracy of rules generated by inductive machine learning algorithm by generating the ensemble of classifiers. It generates multiple classifiers using the CLIP4 algorithm and combines them using a voting scheme. The generation of a set of different classifiers is performed by injecting controlled randomness into the learning algorithm, but without modifying the training data set. Our method is based on the characteristic properties of the CLIP4 algorithm. The case study of the SPECT heart image analysis system is used as an example where improving accuracy is very important. Benchmarking results on other well-known machine learning datasets, and comparison with an algorithm that uses boosting technique to improve its accuracy are also presented. The proposed method always improves the accuracy of the results when compared with the accuracy of a single classifier generated by the CLIP4 algorithm, as opposed to using boosting. The obtained results are comparable with other state-of-the-art machine learning algorithms.

  16. A Genetic Algorithm Approach to Recognise Students' Learning Styles

    ERIC Educational Resources Information Center

    Yannibelli, Virginia; Godoy, Daniela; Amandi, Analia

    2006-01-01

    Learning styles encapsulate the preferences of the students, regarding how they learn. By including information about the student learning style, computer-based educational systems are able to adapt a course according to the individual characteristics of the students. In accomplishing this goal, educational systems have been mostly based on the…

  17. The Topography Tub Learning Activity

    NASA Astrophysics Data System (ADS)

    Glesener, G. B.

    2014-12-01

    Understanding the basic elements of a topographic map (i.e. contour lines and intervals) is just a small part of learning how to use this abstract representational system as a resource in geologic mapping. Interpretation of a topographic map and matching its features with real-world structures requires that the system is utilized for visualizing the shapes of these structures and their spatial orientation. To enrich students' skills in visualizing topography from topographic maps a spatial training activity has been developed that uses 3D objects of various shapes and sizes, a sighting tool, a plastic basin, water, and transparencies. In the first part of the activity, the student is asked to draw a topographic map of one of the 3D objects. Next, the student places the object into a plastic tub in which water is added to specified intervals of height. The shoreline at each interval is used to reference the location of the contour line the student draws on a plastic inkjet transparency directly above the object. A key part of this activity is the use of a sighting tool by the student to assist in keeping the pencil mark directly above the shoreline. It (1) ensures the accurate positioning of the contour line and (2) gives the learner experience with using a sight before going out into the field. Finally, after the student finishes drawing the contour lines onto the transparency, the student can compare and contrast the two maps in order to discover where improvements in their visualization of the contours can be made. The teacher and/or peers can also make suggestions on ways to improve. A number of objects with various shapes and sizes are used in this exercise to produce contour lines representing the different types of topography the student may encounter while field mapping. The intended outcome from using this visualization training activity is improvement in performance of visualizing topography as the student moves between the topographic representation and

  18. The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas.

    PubMed

    Bothe, Melanie K; Dickens, Luke; Reichel, Katrin; Tellmann, Arn; Ellger, Björn; Westphal, Martin; Faisal, Ahmed A

    2013-09-01

    Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient.

  19. Diagnosing Learners' Problem-Solving Strategies Using Learning Environments with Algorithmic Problems in Secondary Education

    ERIC Educational Resources Information Center

    Kiesmuller, Ulrich

    2009-01-01

    At schools special learning and programming environments are often used in the field of algorithms. Particularly with regard to computer science lessons in secondary education, they are supposed to help novices to learn the basics of programming. In several parts of Germany (e.g., Bavaria) these fundamentals are taught as early as in the seventh…

  20. A Computer Environment for Beginners' Learning of Sorting Algorithms: Design and Pilot Evaluation

    ERIC Educational Resources Information Center

    Kordaki, M.; Miatidis, M.; Kapsampelis, G.

    2008-01-01

    This paper presents the design, features and pilot evaluation study of a web-based environment--the SORTING environment--for the learning of sorting algorithms by secondary level education students. The design of this environment is based on modeling methodology, taking into account modern constructivist and social theories of learning while at…

  1. An Improved Force Feedback Control Algorithm for Active Tendons

    PubMed Central

    Guo, Tieneng; Liu, Zhifeng; Cai, Ligang

    2012-01-01

    An active tendon, consisting of a displacement actuator and a co-located force sensor, has been adopted by many studies to suppress the vibration of large space flexible structures. The damping, provided by the force feedback control algorithm in these studies, is small and can increase, especially for tendons with low axial stiffness. This study introduces an improved force feedback algorithm, which is based on the idea of velocity feedback. The algorithm provides a large damping ratio for space flexible structures and does not require a structure model. The effectiveness of the algorithm is demonstrated on a structure similar to JPL-MPI. The results show that large damping can be achieved for the vibration control of large space structures. PMID:23112660

  2. Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond.

    PubMed

    Morita, Kenji; Jitsev, Jenia; Morrison, Abigail

    2016-09-15

    Value-based action selection has been suggested to be realized in the corticostriatal local circuits through competition among neural populations. In this article, we review theoretical and experimental studies that have constructed and verified this notion, and provide new perspectives on how the local-circuit selection mechanisms implement reinforcement learning (RL) algorithms and computations beyond them. The striatal neurons are mostly inhibitory, and lateral inhibition among them has been classically proposed to realize "Winner-Take-All (WTA)" selection of the maximum-valued action (i.e., 'max' operation). Although this view has been challenged by the revealed weakness, sparseness, and asymmetry of lateral inhibition, which suggest more complex dynamics, WTA-like competition could still occur on short time scales. Unlike the striatal circuit, the cortical circuit contains recurrent excitation, which may enable retention or temporal integration of information and probabilistic "soft-max" selection. The striatal "max" circuit and the cortical "soft-max" circuit might co-implement an RL algorithm called Q-learning; the cortical circuit might also similarly serve for other algorithms such as SARSA. In these implementations, the cortical circuit presumably sustains activity representing the executed action, which negatively impacts dopamine neurons so that they can calculate reward-prediction-error. Regarding the suggested more complex dynamics of striatal, as well as cortical, circuits on long time scales, which could be viewed as a sequence of short WTA fragments, computational roles remain open: such a sequence might represent (1) sequential state-action-state transitions, constituting replay or simulation of the internal model, (2) a single state/action by the whole trajectory, or (3) probabilistic sampling of state/action.

  3. Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

    PubMed

    Jankovic, Marko; Ogawa, Hidemitsu

    2004-10-01

    Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

  4. The applications of machine learning algorithms in the modeling of estrogen-like chemicals.

    PubMed

    Liu, Huanxiang; Yao, Xiaojun; Gramatica, Paola

    2009-06-01

    Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that, in the environment, are adversely affecting human and wildlife health through a variety of mechanisms, mainly estrogen receptor-mediated mechanisms of toxicity. Because of the large number of such chemicals in the environment, there is a great need for an effective means of rapidly assessing endocrine-disrupting activity in the toxicology assessment process. When faced with the challenging task of screening large libraries of molecules for biological activity, the benefits of computational predictive models based on quantitative structure-activity relationships to identify possible estrogens become immediately obvious. Recently, in order to improve the accuracy of prediction, some machine learning techniques were introduced to build more effective predictive models. In this review we will focus our attention on some recent advances in the use of these methods in modeling estrogen-like chemicals. The advantages and disadvantages of the machine learning algorithms used in solving this problem, the importance of the validation and performance assessment of the built models as well as their applicability domains will be discussed.

  5. Active dictionary learning for image representation

    NASA Astrophysics Data System (ADS)

    Wu, Tong; Sarwate, Anand D.; Bajwa, Waheed U.

    2015-05-01

    Sparse representations of images in overcomplete bases (i.e., redundant dictionaries) have many applications in computer vision and image processing. Recent works have demonstrated improvements in image representations by learning a dictionary from training data instead of using a predefined one. But learning a sparsifying dictionary can be computationally expensive in the case of a massive training set. This paper proposes a new approach, termed active screening, to overcome this challenge. Active screening sequentially selects subsets of training samples using a simple heuristic and adds the selected samples to a "learning pool," which is then used to learn a newer dictionary for improved representation performance. The performance of the proposed active dictionary learning approach is evaluated through numerical experiments on real-world image data; the results of these experiments demonstrate the effectiveness of the proposed method.

  6. Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

    PubMed Central

    Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

    2016-01-01

    Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

  7. Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method

    NASA Astrophysics Data System (ADS)

    Li, Xiang-Tao; Yin, Ming-Hao

    2012-05-01

    We study the parameter estimation of a nonlinear chaotic system, which can be essentially formulated as a multidimensional optimization problem. In this paper, an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy. Experiments are conducted on the Lorenz system and the Chen system. The proposed algorithm is used to estimate the parameters for these two systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.

  8. A Hybrid Approach to Active Learning.

    ERIC Educational Resources Information Center

    Ramsier, R. D.

    2001-01-01

    Describes an approach to incorporate active learning strategies into the first semester of a university-level introductory physics course. Combines cooperative and peer-based methods inside the classroom with project-based learning outside the classroom in an attempt to develop students' transferable skills as well as improving their understanding…

  9. Child Development: An Active Learning Approach

    ERIC Educational Resources Information Center

    Levine, Laura E.; Munsch, Joyce

    2010-01-01

    Within each chapter of this innovative topical text, the authors engage students by demonstrating the wide range of real-world applications of psychological research connected to child development. In particular, the distinctive Active Learning features incorporated throughout the book foster a dynamic and personal learning process for students.…

  10. Discussing Active Learning from the Practitioner's Perspective

    ERIC Educational Resources Information Center

    Bamba, Priscilla

    2015-01-01

    The purpose of this paper is to present an overview of how active learning took place in a class containing specific readings,cooperative and collaborative group work, and a writing assignment for college students at a Northern Virginia Community College campus (NVCC). Requisite knowledge, skills, learner characteristics, brain-based learning, and…

  11. Active Learning through Toy Design and Development

    ERIC Educational Resources Information Center

    Sirinterlikci, Arif; Zane, Linda; Sirinterlikci, Aleea L.

    2009-01-01

    This article presents an initiative that is based on active learning pedagogy by engaging elementary and middle school students in the toy design and development field. The case study presented in this article is about student learning experiences during their participation in the TOYchallenge National Toy Design Competition. Students followed the…

  12. Conditions for Apprentices' Learning Activities at Work

    ERIC Educational Resources Information Center

    Messmann, Gerhard; Mulder, Regina H.

    2015-01-01

    The aim of this study was to investigate how apprentices' learning activities at work can be fostered. This is a crucial issue as learning at work enhances apprentices' competence development and prepares them for professional development on the job. Therefore, we conducted a study with 70 apprentices in the German dual system and examined the…

  13. Active learning in transportation engineering education

    NASA Astrophysics Data System (ADS)

    Weir, Jennifer Anne

    The objectives of this research were (1) to develop experimental active-based-learning curricula for undergraduate courses in transportation engineering and (2) to assess the effectiveness of an active-learning-based traffic engineering curriculum through an educational experiment. The researcher developed a new highway design course as a pilot study to test selected active-learning techniques before employing them in the traffic engineering curriculum. Active-learning techniques, including multiple-choice questions, short problems completed by individual students or small groups, and group discussions, were used as active interludes within lectures. The researcher also collected and analyzed student performance and attitude data from control and experimental classes to evaluate the relative effectiveness of the traditional lecture (control) approach and the active-learning (experimental) approach. The results indicate that the active-learning approach adopted for the experimental class did have a positive impact on student performance as measured by exam scores. The students in the experimental class also indicated slightly more positive attitudes at the end of the course than the control class, although the difference was not significant. The author recommends that active interludes similar to those in the experimental curricula be used in other courses in civil engineering.

  14. Margin based ontology sparse vector learning algorithm and applied in biology science.

    PubMed

    Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

    2017-01-01

    In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

  15. An active set algorithm for tracing parametrized optima

    NASA Technical Reports Server (NTRS)

    Rakowska, J.; Haftka, R. T.; Watson, L. T.

    1991-01-01

    Optimization problems often depend on parameters that define constraints or objective functions. It is often necessary to know the effect of a change in a parameter on the optimum solution. An algorithm is presented here for tracking paths of optimal solutions of inequality constrained nonlinear programming problems as a function of a parameter. The proposed algorithm employs homotopy zero-curve tracing techniques to track segments where the set of active constraints is unchanged. The transition between segments is handled by considering all possible sets of active constraints and eliminating nonoptimal ones based on the signs of the Lagrange multipliers and the derivatives of the optimal solutions with respect to the parameter. A spring-mass problem is used to illustrate all possible kinds of transition events, and the algorithm is applied to a well-known ten-bar truss structural optimization problem.

  16. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

    PubMed Central

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738

  17. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

    PubMed

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-05-21

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

  18. Applying active learning to assertion classification of concepts in clinical text.

    PubMed

    Chen, Yukun; Mani, Subramani; Xu, Hua

    2012-04-01

    Supervised machine learning methods for clinical natural language processing (NLP) research require a large number of annotated samples, which are very expensive to build because of the involvement of physicians. Active learning, an approach that actively samples from a large pool, provides an alternative solution. Its major goal in classification is to reduce the annotation effort while maintaining the quality of the predictive model. However, few studies have investigated its uses in clinical NLP. This paper reports an application of active learning to a clinical text classification task: to determine the assertion status of clinical concepts. The annotated corpus for the assertion classification task in the 2010 i2b2/VA Clinical NLP Challenge was used in this study. We implemented several existing and newly developed active learning algorithms and assessed their uses. The outcome is reported in the global ALC score, based on the Area under the average Learning Curve of the AUC (Area Under the Curve) score. Results showed that when the same number of annotated samples was used, active learning strategies could generate better classification models (best ALC-0.7715) than the passive learning method (random sampling) (ALC-0.7411). Moreover, to achieve the same classification performance, active learning strategies required fewer samples than the random sampling method. For example, to achieve an AUC of 0.79, the random sampling method used 32 samples, while our best active learning algorithm required only 12 samples, a reduction of 62.5% in manual annotation effort.

  19. SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM

    SciTech Connect

    Bobra, M. G.; Couvidat, S.

    2015-01-10

    We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a database of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.

  20. Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

    SciTech Connect

    Barhen, J.; Cogswell, R.; Protopopescu, V.

    1999-07-31

    A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

  1. Point-of-Purchase Advertising. Learning Activity.

    ERIC Educational Resources Information Center

    Shackelford, Ray

    1998-01-01

    In this technology education activity, students learn the importance of advertising, conduct a day-long survey of advertising strategies, and design and produce a tabletop point-of-purchase advertisement. (JOW)

  2. Dopamine, reward learning, and active inference

    PubMed Central

    FitzGerald, Thomas H. B.; Dolan, Raymond J.; Friston, Karl

    2015-01-01

    Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings. PMID:26581305

  3. Javascript Library for Developing Interactive Micro-Level Animations for Teaching and Learning Algorithms on One-Dimensional Arrays

    ERIC Educational Resources Information Center

    Végh, Ladislav

    2016-01-01

    The first data structure that first-year undergraduate students learn during the programming and algorithms courses is the one-dimensional array. For novice programmers, it might be hard to understand different algorithms on arrays (e.g. searching, mirroring, sorting algorithms), because the algorithms dynamically change the values of elements. In…

  4. Interactive Algorithms for Teaching and Learning Acute Medicine in the Network of Medical Faculties MEFANET

    PubMed Central

    Štourač, Petr; Komenda, Martin; Harazim, Hana; Kosinová, Martina; Gregor, Jakub; Hůlek, Richard; Smékalová, Olga; Křikava, Ivo; Štoudek, Roman; Dušek, Ladislav

    2013-01-01

    Background Medical Faculties Network (MEFANET) has established itself as the authority for setting standards for medical educators in the Czech Republic and Slovakia, 2 independent countries with similar languages that once comprised a federation and that still retain the same curricular structure for medical education. One of the basic goals of the network is to advance medical teaching and learning with the use of modern information and communication technologies. Objective We present the education portal AKUTNE.CZ as an important part of the MEFANET’s content. Our focus is primarily on simulation-based tools for teaching and learning acute medicine issues. Methods Three fundamental elements of the MEFANET e-publishing system are described: (1) medical disciplines linker, (2) authentication/authorization framework, and (3) multidimensional quality assessment. A new set of tools for technology-enhanced learning have been introduced recently: Sandbox (works in progress), WikiLectures (collaborative content authoring), Moodle-MEFANET (central learning management system), and Serious Games (virtual casuistics and interactive algorithms). The latest development in MEFANET is designed for indexing metadata about simulation-based learning objects, also known as electronic virtual patients or virtual clinical cases. The simulations assume the form of interactive algorithms for teaching and learning acute medicine. An anonymous questionnaire of 10 items was used to explore students’ attitudes and interests in using the interactive algorithms as part of their medical or health care studies. Data collection was conducted over 10 days in February 2013. Results In total, 25 interactive algorithms in the Czech and English languages have been developed and published on the AKUTNE.CZ education portal to allow the users to test and improve their knowledge and skills in the field of acute medicine. In the feedback survey, 62 participants completed the online questionnaire (13

  5. People with Learning Disabilities and "Active Ageing"

    ERIC Educational Resources Information Center

    Foster, Liam; Boxall, Kathy

    2015-01-01

    Background: People (with and without learning disabilities) are living longer. Demographic ageing creates challenges and the leading policy response to these challenges is "active ageing". "Active" does not just refer to the ability to be physically and economically active, but also includes ongoing social and civic engagement…

  6. Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.

    PubMed

    Bourke, Alan K; Klenk, Jochen; Schwickert, Lars; Aminian, Kamiar; Ihlen, Espen A F; Mellone, Sabato; Helbostad, Jorunn L; Chiari, Lorenzo; Becker, Clemens

    2016-08-01

    Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.

  7. Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

    NASA Astrophysics Data System (ADS)

    Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

    2015-12-01

    In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

  8. Active Learning through Online Instruction

    ERIC Educational Resources Information Center

    Gulbahar, Yasemin; Kalelioglu, Filiz

    2010-01-01

    This article explores the use of proper instructional techniques in online discussions that lead to meaningful learning. The research study looks at the effective use of two instructional techniques within online environments, based on qualitative measures. "Brainstorming" and "Six Thinking Hats" were selected and implemented…

  9. Teacher Directed Active Learning Games

    ERIC Educational Resources Information Center

    Scarlatos, Lori L.; Scarlatos, Tony

    2009-01-01

    Games are widely recognized for their potential to enhance students' learning. Yet they are only rarely used in classrooms because they cannot be modified to meet the needs of a particular class. This article describes a novel approach to creating educational software that addresses this problem: provide an interface specifically for teachers that…

  10. A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem

    SciTech Connect

    Bayardo, R.J. Jr.; Miranker, D.P.

    1996-12-31

    Learning during backtrack search is a space-intensive process that records information (such as additional constraints) in order to avoid redundant work. In this paper, we analyze the effects of polynomial-space-bounded learning on runtime complexity of backtrack search. One space-bounded learning scheme records only those constraints with limited size, and another records arbitrarily large constraints but deletes those that become irrelevant to the portion of the search space being explored. We find that relevance-bounded learning allows better runtime bounds than size-bounded learning on structurally restricted constraint satisfaction problems. Even when restricted to linear space, our relevance-bounded learning algorithm has runtime complexity near that of unrestricted (exponential space-consuming) learning schemes.

  11. Teachers' Everyday Professional Development: Mapping Informal Learning Activities, Antecedents, and Learning Outcomes

    ERIC Educational Resources Information Center

    Kyndt, Eva; Gijbels, David; Grosemans, Ilke; Donche, Vincent

    2016-01-01

    Although a lot is known about teacher development by means of formal learning activities, research on teachers' everyday learning is limited. In the current systematic review, we analyzed 74 studies focusing on teachers' informal learning to identify teachers' learning activities, antecedents for informal learning, and learning outcomes. In…

  12. Discovery Learning in Autonomous Agents Using Genetic Algorithms

    DTIC Science & Technology

    1993-12-01

    School of Engineering of the Air Force Institute of Technology Air University In Partial Fulfillment of the Requirements for the Degree of Master of...Science in Electrical Engineering Edward 0. Gordon, B.S. Capt December 1993 Approved for public release; distribution unlimited Acknowledgements A document... engineer , since the learning system can, in many cases, teach itself. And such discovery learning could reveal new techniques or perhaps weaknewses in

  13. Manifold Regularized Experimental Design for Active Learning.

    PubMed

    Zhang, Lining; Shum, Hubert P H; Shao, Ling

    2016-12-02

    Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

  14. Actively learning object names across ambiguous situations.

    PubMed

    Kachergis, George; Yu, Chen; Shiffrin, Richard M

    2013-01-01

    Previous research shows that people can use the co-occurrence of words and objects in ambiguous situations (i.e., containing multiple words and objects) to learn word meanings during a brief passive training period (Yu & Smith, 2007). However, learners in the world are not completely passive but can affect how their environment is structured by moving their heads, eyes, and even objects. These actions can indicate attention to a language teacher, who may then be more likely to name the attended objects. Using a novel active learning paradigm in which learners choose which four objects they would like to see named on each successive trial, this study asks whether active learning is superior to passive learning in a cross-situational word learning context. Finding that learners perform better in active learning, we investigate the strategies and discover that most learners use immediate repetition to disambiguate pairings. Unexpectedly, we find that learners who repeat only one pair per trial--an easy way to infer this pair-perform worse than those who repeat multiple pairs per trial. Using a working memory extension to an associative model of word learning with uncertainty and familiarity biases, we investigate individual differences that correlate with these assorted strategies.

  15. Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

    PubMed

    Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert

    2010-03-01

    We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 10(6) × 10(6) incomplete matrix with 10(5) observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques.

  16. Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images

    NASA Astrophysics Data System (ADS)

    Charrier, Christophe; Saadane, AbdelHakim; Fernandez-Maloigne, Christine

    2015-01-01

    No-reference image quality metrics are of fundamental interest as they can be embedded in practical applications. The main goal of this paper is to perform a comparative study of seven well known no-reference learning-based image quality algorithms. To test the performance of these algorithms, three public databases are used. As a first step, the trial algorithms are compared when no new learning is performed. The second step investigates how the training set influences the results. The Spearman Rank Ordered Correlation Coefficient (SROCC) is utilized to measure and compare the performance. In addition, an hypothesis test is conducted to evaluate the statistical significance of performance of each tested algorithm.

  17. An efficient approximation algorithm for finding a maximum clique using Hopfield network learning.

    PubMed

    Wang, Rong Long; Tang, Zheng; Cao, Qi Ping

    2003-07-01

    In this article, we present a solution to the maximum clique problem using a gradient-ascent learning algorithm of the Hopfield neural network. This method provides a near-optimum parallel algorithm for finding a maximum clique. To do this, we use the Hopfield neural network to generate a near-maximum clique and then modify weights in a gradient-ascent direction to allow the network to escape from the state of near-maximum clique to maximum clique or better. The proposed parallel algorithm is tested on two types of random graphs and some benchmark graphs from the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). The simulation results show that the proposed learning algorithm can find good solutions in reasonable computation time.

  18. Derivation of a Novel Efficient Supervised Learning Algorithm from Cortical-Subcortical Loops

    PubMed Central

    Chandrashekar, Ashok; Granger, Richard

    2012-01-01

    Although brain circuits presumably carry out powerful perceptual algorithms, few instances of derived biological methods have been found to compete favorably against algorithms that have been engineered for specific applications. We forward a novel analysis of a subset of functions of cortical–subcortical loops, which constitute more than 80% of the human brain, thus likely underlying a broad range of cognitive functions. We describe a family of operations performed by the derived method, including a non-standard method for supervised classification, which may underlie some forms of cortically dependent associative learning. The novel supervised classifier is compared against widely used algorithms for classification, including support vector machines (SVM) and k-nearest neighbor methods, achieving corresponding classification rates – at a fraction of the time and space costs. This represents an instance of a biologically derived algorithm comparing favorably against widely used machine learning methods on well-studied tasks. PMID:22291632

  19. Active Semi-Supervised Learning Method with Hybrid Deep Belief Networks

    PubMed Central

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively. PMID:25208128

  20. Active semi-supervised learning method with hybrid deep belief networks.

    PubMed

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  1. A fully complex-valued radial basis function network and its learning algorithm.

    PubMed

    Savitha, R; Suresh, S; Sundararajan, N

    2009-08-01

    In this paper, a fully complex-valued radial basis function (FC-RBF) network with a fully complex-valued activation function has been proposed, and its complex-valued gradient descent learning algorithm has been developed. The fully complex activation function, sech(.) of the proposed network, satisfies all the properties needed for a complex-valued activation function and has Gaussian-like characteristics. It maps C(n) --> C, unlike the existing activation functions of complex-valued RBF network that maps C(n) --> R. Since the performance of the complex-RBF network depends on the number of neurons and initialization of network parameters, we propose a K-means clustering based neuron selection and center initialization scheme. First, we present a study on convergence using complex XOR problem. Next, we present a synthetic function approximation problem and the two-spiral classification problem. Finally, we present the results for two practical applications, viz., a non-minimum phase equalization and an adaptive beam-forming problem. The performance of the network was compared with other well-known complex-valued RBF networks available in literature, viz., split-complex CRBF, CMRAN and the CELM. The results indicate that the proposed fully complex-valued network has better convergence, approximation and classification ability.

  2. Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm

    NASA Astrophysics Data System (ADS)

    Bobra, M.; Couvidat, S. P.

    2014-12-01

    We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called Support Vector Machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space (Schou et al., 2012). Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time such a large dataset of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a database of 2,071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters --- which include the flux, energy, shear, current, helicity, gradient, geometry, and Lorentz force. We then train and test the machine-learning algorithm. Finally, we estimate the performance of this algorithm using forecast verification metrics with an emphasis on the true skill statistic (TSS). Bloomfield et al. (2012) suggest the use of the TSS as it is not sensitive to the class imbalance problem. Indeed, there are many more non-flaring active regions in a given time interval than flaring ones: this class imbalance distorts many performance metrics and renders comparison between various studies somewhat unreliable. We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.

  3. Quantum Speedup for Active Learning Agents

    NASA Astrophysics Data System (ADS)

    Paparo, Giuseppe Davide; Dunjko, Vedran; Makmal, Adi; Martin-Delgado, Miguel Angel; Briegel, Hans J.

    2014-07-01

    Can quantum mechanics help us build intelligent learning agents? A defining signature of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in real-life situations is the size and complexity of the corresponding task environment. Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here, we show that quantum physics can help and provide a quadratic speedup for active learning as a genuine problem of artificial intelligence. This result will be particularly relevant for applications involving complex task environments.

  4. A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning.

    PubMed

    Wu, Xing; Rózycki, Paweł; Wilamowski, Bogdan M

    2015-08-01

    Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determining network size and training the parameters. Most current algorithms could not be satisfactory to both sides. Some algorithms focused on construction and only tuned part of the parameters, which may not be able to achieve a compact network. Other gradient-based optimization algorithms focused on parameters tuning while the network size has to be preset by the user. Therefore, trial-and-error approach has to be used to search the optimal network size. Because results of each trial cannot be reused in another trial, it costs much computation. In this paper, a hybrid constructive (HC)algorithm is proposed for SLFN learning, which can train all the parameters and determine the network size simultaneously. At first, by combining Levenberg-Marquardt algorithm and least-square method, a hybrid algorithm is presented for training SLFN with fixed network size. Then,with the hybrid algorithm, an incremental constructive scheme is proposed. A new randomly initialized neuron is added each time when the training entrapped into local minima. Because the training continued on previous results after adding new neurons, the proposed HC algorithm works efficiently. Several practical problems were given for comparison with other popular algorithms. The experimental results demonstrated that the HC algorithm worked more efficiently than those optimization methods with trial and error, and could achieve much more compact SLFN than those construction algorithms.

  5. Asymmetric Variate Generation via a Parameterless Dual Neural Learning Algorithm

    PubMed Central

    Fiori, Simone

    2008-01-01

    In a previous work (S. Fiori, 2006), we proposed a random number generator based on a tunable non-linear neural system, whose learning rule is designed on the basis of a cardinal equation from statistics and whose implementation is based on look-up tables (LUTs). The aim of the present manuscript is to improve the above-mentioned random number generation method by changing the learning principle, while retaining the efficient LUT-based implementation. The new method proposed here proves easier to implement and relaxes some previous limitations. PMID:18483612

  6. 3D Visualization of Machine Learning Algorithms with Astronomical Data

    NASA Astrophysics Data System (ADS)

    Kent, Brian R.

    2016-01-01

    We present innovative machine learning (ML) methods using unsupervised clustering with minimum spanning trees (MSTs) to study 3D astronomical catalogs. Utilizing Python code to build trees based on galaxy catalogs, we can render the results with the visualization suite Blender to produce interactive 360 degree panoramic videos. The catalogs and their ML results can be explored in a 3D space using mobile devices, tablets or desktop browsers. We compare the statistics of the MST results to a number of machine learning methods relating to optimization and efficiency.

  7. Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions

    NASA Astrophysics Data System (ADS)

    Klivans, Adam R.; Long, Philip M.; Tang, Alex K.

    In 1990, E. Baum gave an elegant polynomial-time algorithm for learning the intersection of two origin-centered halfspaces with respect to any symmetric distribution (i.e., any {\\cal D} such that {\\cal D}(E) = {\\cal D}(-E)) [3]. Here we prove that his algorithm also succeeds with respect to any mean zero distribution with a log-concave density (a broad class of distributions that need not be symmetric). As far as we are aware, prior to this work, it was not known how to efficiently learn any class of intersections of halfspaces with respect to log-concave distributions.

  8. Bacteriophage: A Model System for Active Learning

    PubMed Central

    LUCIANO, CARL S.; YOUNG, MATTHEW W.; PATTERSON, ROBIN R.

    2002-01-01

    Although bacteriophage provided a useful model system for the development of molecular biology, its simplicity, accessibility, and familiarity have not been fully exploited in the classroom. We describe a student-centered laboratory course in which student teams selected phage from sewage samples and characterized the phage in a semester-long project that modeled real-life scientific research. The course used an instructional approach that included active learning, collaboration, and learning by inquiry. Cooperative student teams had primary responsibility for organizing the content of the course, writing to learn using a journal article format, involving the entire group in shared laboratory responsibilities, and applying knowledge to the choice of new experiments. The results of student evaluations indicated a high level of satisfaction with the course. Our positive experience with this course suggests that phage provides an attractive model system for an active-learning classroom. PMID:23653543

  9. Active learning in optics and photonics

    NASA Astrophysics Data System (ADS)

    Niemela, Joseph J.

    2016-09-01

    Active learning in optics and photonics (ALOP) is a program of the International Basic Sciences Program at UNESCO, in collaboration with the Abdus Salam International Centre for Theoretical Physics (ICTP) and supported by SPIE, which is designed to help teachers in the developing world attract and retain students in the physical sciences. Using optics and photonics, it naturally attracts the interest of students and can be implemented using relatively low cost technologies, so that it can be more easily reproduced locally. The active learning methodology is student-centered, meaning the teachers give up the role of lecturer in favor of guiding and facilitating a learning process in which students engage in hands-on activities and active peer-peer discussions, and is shown to effectively enhance basic conceptual understanding of physics.

  10. Is Peer Interaction Necessary for Optimal Active Learning?

    ERIC Educational Resources Information Center

    Linton, Debra L.; Farmer, Jan Keith; Peterson, Ernie

    2014-01-01

    Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of…

  11. Karyotype Analysis Activity: A Constructivist Learning Design

    ERIC Educational Resources Information Center

    Ahmed, Noveera T.

    2015-01-01

    This classroom activity is based on a constructivist learning design and engages students in physically constructing a karyotype of three mock patients. Students then diagnose the chromosomal aneuploidy based on the karyotype, list the symptoms associated with the disorder, and discuss the implications of the diagnosis. This activity is targeted…

  12. RoboResource Technology Learning Activities.

    ERIC Educational Resources Information Center

    Keck, Tom, Comp.; Frye, Ellen, Ed.

    Preparing students to be successful in a rapidly changing world means showing them how to use the tools of technology and how to integrate those tools into all areas of learning. This booklet is divided into three sections: Design Activities, Experiments, and Resources. The design activities ask students to collaborate on design projects. In these…

  13. Oral Hygiene. Instructor's Packet. Learning Activity Package.

    ERIC Educational Resources Information Center

    Hime, Kirsten

    This instructor's packet accompanies the learning activity package (LAP) on oral hygiene. Contents included in the packet are a time sheet, suggested uses for the LAP, an instruction sheet, final LAP reviews, a final LAP review answer key, suggested activities, additional resources (student handouts), student performance checklists for both…

  14. Learning Activities for the Growth Season.

    ERIC Educational Resources Information Center

    Darby, Linda, Ed.

    This poster, illustrated with a graphic of a caterpillar changing to a cocoon and emerging as a butterfly, presents learning activities for 7 weeks based on the seven stages of growth in the President's "Call to Action." Each week includes 5 days of activities based on seven themes: (1) "Reading on Your Own"; (2) "Getting…

  15. An improved teaching-learning based robust edge detection algorithm for noisy images.

    PubMed

    Thirumavalavan, Sasirooba; Jayaraman, Sasikala

    2016-11-01

    This paper presents an improved Teaching Learning Based Optimization (TLO) and a methodology for obtaining the edge maps of the noisy real life digital images. TLO is a population based algorithm that simulates the teaching-learning mechanism in class rooms, comprising two phases of teaching and learning. The 'Teaching Phase' represents learning from the teacher and 'Learning Phase' indicates learning by the interaction between learners. This paper introduces a third phase denoted by "Avoiding Phase" that helps to keep the learners away from the worst students with a view of exploring the problem space more effectively and escaping from the sub-optimal solutions. The improved TLO (ITLO) explores the solution space and provides the global best solution. The edge detection problem is formulated as an optimization problem and solved using the ITLO. The results of real life and medical images illustrate the performance of the developed method.

  16. Batch Mode Active Learning for Regression With Expected Model Change.

    PubMed

    Cai, Wenbin; Zhang, Muhan; Zhang, Ya

    2016-04-20

    While active learning (AL) has been widely studied for classification problems, limited efforts have been done on AL for regression. In this paper, we introduce a new AL framework for regression, expected model change maximization (EMCM), which aims at choosing the unlabeled data instances that result in the maximum change of the current model once labeled. The model change is quantified as the difference between the current model parameters and the updated parameters after the inclusion of the newly selected examples. In light of the stochastic gradient descent learning rule, we approximate the change as the gradient of the loss function with respect to each single candidate instance. Under the EMCM framework, we propose novel AL algorithms for the linear and nonlinear regression models. In addition, by simulating the behavior of the sequential AL policy when applied for k iterations, we further extend the algorithms to batch mode AL to simultaneously choose a set of k most informative instances at each query time. Extensive experimental results on both UCI and StatLib benchmark data sets have demonstrated that the proposed algorithms are highly effective and efficient.

  17. Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm.

    PubMed

    Wang, Li Jia; Zhang, Hua

    2016-01-01

    An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.

  18. Active learning framework with iterative clustering for bioimage classification.

    PubMed

    Kutsuna, Natsumaro; Higaki, Takumi; Matsunaga, Sachihiro; Otsuki, Tomoshi; Yamaguchi, Masayuki; Fujii, Hirofumi; Hasezawa, Seiichiro

    2012-01-01

    Advances in imaging systems have yielded a flood of images into the research field. A semi-automated facility can reduce the laborious task of classifying this large number of images. Here we report the development of a novel framework, CARTA (Clustering-Aided Rapid Training Agent), applicable to bioimage classification that facilitates annotation and selection of features. CARTA comprises an active learning algorithm combined with a genetic algorithm and self-organizing map. The framework provides an easy and interactive annotation method and accurate classification. The CARTA framework enables classification of subcellular localization, mitotic phases and discrimination of apoptosis in images of plant and human cells with an accuracy level greater than or equal to annotators. CARTA can be applied to classification of magnetic resonance imaging of cancer cells or multicolour time-course images after surgery. Furthermore, CARTA can support development of customized features for classification, high-throughput phenotyping and application of various classification schemes dependent on the user's purpose.

  19. Design and Implementation of an Object Oriented Learning Activity System

    ERIC Educational Resources Information Center

    Lin, Huan-Yu; Tseng, Shian-Shyong; Weng, Jui-Feng; Su, Jun-Ming

    2009-01-01

    With the development of e-learning technology, many specifications of instructional design have been proposed to make learning activity sharable and reusable. With the specifications and sufficient learning resources, the researches further focus on how to provide learners more appropriate learning activities to improve their learning performance.…

  20. Annotating smart environment sensor data for activity learning.

    PubMed

    Szewcyzk, S; Dwan, K; Minor, B; Swedlove, B; Cook, D

    2009-01-01

    The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track the activities that people perform at home. Machine learning techniques can perform this task, but the software algorithms rely upon large amounts of sample data that is correctly labeled with the corresponding activity. Labeling, or annotating, sensor data with the corresponding activity can be time consuming, may require input from the smart home resident, and is often inaccurate. Therefore, in this paper we investigate four alternative mechanisms for annotating sensor data with a corresponding activity label. We evaluate the alternative methods along the dimensions of annotation time, resident burden, and accuracy using sensor data collected in a real smart apartment.

  1. Multiplatform GPGPU implementation of the active contours without edges algorithm

    NASA Astrophysics Data System (ADS)

    Zavala-Romero, Olmo; Meyer-Baese, Anke; Meyer-Baese, Uwe

    2012-05-01

    An OpenCL implementation of the Active Contours Without Edges algorithm is presented. The proposed algorithm uses the General Purpose Computing on Graphics Processing Units (GPGPU) to accelerate the original model by parallelizing the two main steps of the segmentation process, the computation of the Signed Distance Function (SDF) and the evolution of the segmented curve. The proposed scheme for the computation of the SDF is based on the iterative construction of partial Voronoi diagrams of a reduced dimension and obtains the exact Euclidean distance in a time of order O(N/p), where N is the number of pixels and p the number of processors. With high resolution images the segmentation algorithm runs 10 times faster than its equivalent sequential implementation. This work is being done as an open source software that, being programmed in OpenCL, can be used in dierent platforms allowing a broad number of nal users and can be applied in dierent areas of computer vision, like medical imaging, tracking, robotics, etc. This work uses OpenGL to visualize the algorithm results in real time.

  2. Multi-objective teaching-learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

    NASA Astrophysics Data System (ADS)

    Lin, Wenwen; Yu, D. Y.; Wang, S.; Zhang, Chaoyong; Zhang, Sanqiang; Tian, Huiyu; Luo, Min; Liu, Shengqiang

    2015-07-01

    In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching-learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.

  3. Learning plan applicability through active mental entities

    SciTech Connect

    Baroni, Pietro; Fogli, Daniela; Guida, Giovanni

    1999-03-22

    This paper aims at laying down the foundations of a new approach to learning in autonomous mobile robots. It is based on the assumption that robots can be provided with built-in action plans and with mechanisms to modify and improve such plans. This requires that robots are equipped with some form of high-level reasoning capabilities. Therefore, the proposed learning technique is embedded in a novel distributed control architecture featuring an explicit model of robot's cognitive activity. In particular, cognitive activity is obtained by the interaction of active mental entities, such as intentions, persuasions and expectations. Learning capabilities are implemented starting from the interaction of such mental entities. The proposal is illustrated through an example concerning a robot in charge of reaching a target in an unknown environment cluttered with obstacles.

  4. Learning lung nodule similarity using a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Seitz, Kerry A., Jr.; Giuca, Anne-Marie; Furst, Jacob; Raicu, Daniela

    2012-03-01

    The effectiveness and efficiency of content-based image retrieval (CBIR) can be improved by determining an optimal combination of image features to use in determining similarity between images. This combination of features can be optimized using a genetic algorithm (GA). Although several studies have used genetic algorithms to refine image features and similarity measures in CBIR, the present study is the first to apply these techniques to medical image retrieval. By implementing a GA to test different combinations of image features for pulmonary nodules in CT scans, the set of image features was reduced to 29 features from a total of 63 extracted features. The performance of the CBIR system was assessed by calculating the average precision across all query nodules. The precision values obtained using the GA-reduced set of features were significantly higher than those found using all 63 image features. Using radiologist-annotated malignancy ratings as ground truth resulted in an average precision of 85.95% after 3 images retrieved per query nodule when using the feature set identified by the GA. Using computer-predicted malignancy ratings as ground truth resulted in an average precision of 86.91% after 3 images retrieved. The results suggest that in the absence of radiologist semantic ratings, using computer-predicted malignancy as ground truth is a valid substitute given the closeness of the two precision values.

  5. A comparative study of machine learning algorithms applied to predictive toxicology data mining.

    PubMed

    Neagu, Daniel C; Guo, Gongde; Trundle, Paul R; Cronin, Mark T D

    2007-03-01

    This paper reports results of a comparative study of widely used machine learning algorithms applied to predictive toxicology data mining. The machine learning algorithms involved were chosen in terms of their representability and diversity, and were extensively evaluated with seven toxicity data sets which were taken from real-world applications. Some results based on visual analysis of the correlations of different descriptors to the class values of chemical compounds, and on the relationships of the range of chosen descriptors to the performance of machine learning algorithms, are emphasised from our experiments. Some interesting findings relating to the data and the quality of the models are presented--for example, that no specific algorithm appears best for all seven toxicity data sets, and that up to five descriptors are sufficient for creating classification models for each toxicity data set with good accuracy. We suggest that, for a specific data set, model accuracy is affected by the feature selection method and model development technique. Models built with too many or too few descriptors are undesirable, and finding the optimal feature subset appears at least as important as selecting appropriate algorithms with which to build a final model.

  6. A Genetic Algorithm Approach for Group Formation in Collaborative Learning Considering Multiple Student Characteristics

    ERIC Educational Resources Information Center

    Moreno, Julian; Ovalle, Demetrio A.; Vicari, Rosa M.

    2012-01-01

    Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as…

  7. Adjoint-operators and non-adiabatic learning algorithms in neural networks

    NASA Technical Reports Server (NTRS)

    Toomarian, N.; Barhen, J.

    1991-01-01

    Adjoint sensitivity equations are presented, which can be solved simultaneously (i.e., forward in time) with the dynamics of a nonlinear neural network. These equations provide the foundations for a new methodology which enables the implementation of temporal learning algorithms in a highly efficient manner.

  8. Statistical learning algorithms for identifying contrasting tillage practices with landsat thematic mapper data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Tillage management practices have direct impact on water holding capacity, evaporation, carbon sequestration, and water quality. This study examines the feasibility of two statistical learning algorithms, such as Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for cla...

  9. The Relationship between Volume Conservation and the Learning of a Volume Algorithm for a Cuboid.

    ERIC Educational Resources Information Center

    Feghali, Issa

    This study investigated the relationship between the level of conservation of displaced volume and the degree to which sixth graders learn the volume algorithm of a cuboid, i.e., volume = length x width x height (v = l x w x h). The problem is a consequence of an apparent discrepancy between the present school programs and the theory of Piaget…

  10. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

    PubMed Central

    2016-01-01

    Background One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. PMID:27185366

  11. Performance Evaluation of an Option-Based Learning Algorithm in Multi-Car Elevator Systems

    NASA Astrophysics Data System (ADS)

    Valdivielso Chian, Alex; Miyamoto, Toshiyuki

    In this letter, we present the evaluation of an option-based learning algorithm, developed to perform a conflict-free allocation of calls among cars in a multi-car elevator system. We evaluate its performance in terms of the service time, its flexibility in the task-allocation, and the load balancing.

  12. Active Learning in Engineering Education: A (Re)Introduction

    ERIC Educational Resources Information Center

    Lima, Rui M.; Andersson, Pernille Hammar; Saalman, Elisabeth

    2017-01-01

    The informal network "Active Learning in Engineering Education" (ALE) has been promoting Active Learning since 2001. ALE creates opportunity for practitioners and researchers of engineering education to collaboratively learn how to foster learning of engineering students. The activities in ALE are centred on the vision that learners…

  13. A new machine learning algorithm for removal of salt and pepper noise

    NASA Astrophysics Data System (ADS)

    Wang, Yi; Adhami, Reza; Fu, Jian

    2015-07-01

    Supervised machine learning algorithm has been extensively studied and applied to different fields of image processing in past decades. This paper proposes a new machine learning algorithm, called margin setting (MS), for restoring images that are corrupted by salt and pepper impulse noise. Margin setting generates decision surface to classify the noise pixels and non-noise pixels. After the noise pixels are detected, a modified ranked order mean (ROM) filter is used to replace the corrupted pixels for images reconstruction. Margin setting algorithm is tested with grayscale and color images for different noise densities. The experimental results are compared with those of the support vector machine (SVM) and standard median filter (SMF). The results show that margin setting outperforms these methods with higher Peak Signal-to-Noise Ratio (PSNR), lower mean square error (MSE), higher image enhancement factor (IEF) and higher Structural Similarity Index (SSIM).

  14. Developing Fire Detection Algorithms by Geostationary Orbiting Platforms and Machine Learning Techniques

    NASA Astrophysics Data System (ADS)

    Salvador, Pablo; Sanz, Julia; Garcia, Miguel; Casanova, Jose Luis

    2016-08-01

    Fires in general and forest fires specific are a major concern in terms of economical and biological loses. Remote sensing technologies have been focusing on developing several algorithms, adapted to a large kind of sensors, platforms and regions in order to obtain hotspots as faster as possible. The aim of this study is to establish an automatic methodology to develop hotspots detection algorithms with Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor on board Meteosat Second Generation platform (MSG) based on machine learning techniques that can be exportable to others geostationary platforms and sensors and to any area of the Earth. The sensitivity (SE), specificity (SP) and accuracy (AC) parameters have been analyzed in order to develop the final machine learning algorithm taking into account the preferences and final use of the predicted data.

  15. Statistical algorithms for target detection in coherent active polarimetric images.

    PubMed

    Goudail, F; Réfrégier, P

    2001-12-01

    We address the problem of small-target detection with a polarimetric imager that provides orthogonal state contrast images. Such active systems allow one to measure the degree of polarization of the light backscattered by purely depolarizing isotropic materials. To be independent of the spatial nonuniformities of the illumination beam, small-target detection on the orthogonal state contrast image must be performed without using the image of backscattered intensity. We thus propose and develop a simple and efficient target detection algorithm based on a nonlinear pointwise transformation of the orthogonal state contrast image followed by a maximum-likelihood algorithm optimal for additive Gaussian perturbations. We demonstrate the efficiency of this suboptimal technique in comparison with the optimal one, which, however, assumes a priori knowledge about the scene that is not available in practice. We illustrate the performance of this approach on both simulated and real polarimetric images.

  16. Algorithms for Learning Preferences for Sets of Objects

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri L.; desJardins, Marie; Eaton, Eric

    2010-01-01

    A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical

  17. LMS learning algorithms: misconceptions and new results on converence.

    PubMed

    Wang, Z Q; Manry, M T; Schiano, J L

    2000-01-01

    The Widrow-Hoff delta rule is one of the most popular rules used in training neural networks. It was originally proposed for the ADALINE, but has been successfully applied to a few nonlinear neural networks as well. Despite its popularity, there exist a few misconceptions on its convergence properties. In this paper we consider repetitive learning (i.e., a fixed set of samples are used for training) and provide an in-depth analysis in the least mean square (LMS) framework. Our main result is that contrary to common belief, the nonbatch Widrow-Hoff rule does not converge in general. It converges only to a limit cycle.

  18. Astronomy Learning Activities for Tablets

    NASA Astrophysics Data System (ADS)

    Pilachowski, Catherine A.; Morris, Frank

    2015-08-01

    Four web-based tools allow students to manipulate astronomical data to learn concepts in astronomy. The tools are HTML5, CSS3, Javascript-based applications that provide access to the content on iPad and Android tablets. The first tool “Three Color” allows students to combine monochrome astronomical images taken through different color filters or in different wavelength regions into a single color image. The second tool “Star Clusters” allows students to compare images of stars in clusters with a pre-defined template of colors and sizes in order to produce color-magnitude diagrams to determine cluster ages. The third tool adapts Travis Rector’s “NovaSearch” to allow students to examine images of the central regions of the Andromeda Galaxy to find novae. After students find a nova, they are able to measure the time over which the nova fades away. A fourth tool, Proper Pair, allows students to interact with Hipparcos data to evaluate close double stars are physical binaries or chance superpositions. Further information and access to these web-based tools are available at www.astro.indiana.edu/ala/.

  19. Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

    PubMed Central

    Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

    2016-01-01

    Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768

  20. Using Oceanography to Support Active Learning

    NASA Astrophysics Data System (ADS)

    Byfield, V.

    2012-04-01

    Teachers are always on the lookout for material to give their brightest students, in order to keep them occupied, stimulated and challenged, while the teacher gets on with helping the rest. They are also looking for material that can inspire and enthuse those who think that school is 'just boring!' Oceanography, well presented, has the capacity to do both. As a relatively young science, oceanography is not a core curriculum subject (possibly an advantage), but it draws on the traditional sciences of biology, chemistry, physic and geology, and can provide wonderful examples for teaching concepts in school sciences. It can also give good reasons for learning science, maths and technology. Exciting expeditions (research cruises) to far-flung places; opportunities to explore new worlds, a different angle on topical debates such as climate change, pollution, or conservation can bring a new life to old subjects. Access to 'real' data from satellites or Argo floats can be used to develop analytical and problem solving skills. The challenge is to make all this available in a form that can easily be used by teachers and students to enhance the learning experience. We learn by doing. Active teaching methods require students to develop their own concepts of what they are learning. This stimulates new neural connections in the brain - the physical manifestation of learning. There is a large body of evidence to show that active learning is much better remembered and understood. Active learning develops thinking skills through analysis, problem solving, and evaluation. It helps learners to use their knowledge in realistic and useful ways, and see its importance and relevance. Most importantly, properly used, active learning is fun. This paper presents experiences from a number of education outreach projects that have involved the National Oceanography Centre in Southampton, UK. All contain some element of active learning - from quizzes and puzzles to analysis of real data from

  1. Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms.

    PubMed

    Yang, Miin-Shen; Lin, Karen Chia-Ren; Liu, Hsiu-Chih; Lirng, Jiing-Feng

    2007-02-01

    In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.

  2. Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning

    PubMed Central

    Fu, QiMing

    2016-01-01

    To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704

  3. Algorithm Building and Learning Programming Languages Using a New Educational Paradigm

    NASA Astrophysics Data System (ADS)

    Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel

    2011-08-01

    This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.

  4. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

    PubMed Central

    Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

    2016-01-01

    To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. PMID:27669250

  5. Speech modifications algorithms used for training language learning-impaired children.

    PubMed

    Nagarajan, S S; Wang, X; Merzenich, M M; Schreiner, C E; Johnston, P; Jenkins, W M; Miller, S; Tallal, P

    1998-09-01

    In this paper, the details of processing algorithms used in a training program with language learning-impaired children (LLI's) are described. The training program utilized computer games, speech/language training exercises, books-on-tape and educational CD-ROM's. Speech tracks in these materials were processed using these algorithms. During a four week training period, recognition of both processed and normal speech in these children continually increased to near age-appropriate levels. We conclude that this form of processed speech is subject to profound perceptual learning effects and exhibits widespread generalization to normal speech. This form of learning and generalization contributes to the rehabilitation of temporal processing deficits and language comprehension in this subject population.

  6. A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

    DOE PAGES

    Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

    2015-01-31

    Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

  7. A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

    SciTech Connect

    Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; Ukkusuri, Satish V.

    2015-01-31

    Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plans in terms of average delay, number of stops, and vehicular emissions at the network level.

  8. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy

    PubMed Central

    2017-01-01

    Background Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P<.05). Scores did not vary between doctors who were rated as popular or

  9. A Heuristic Algorithm for Planning Personalized Learning Paths for Context-Aware Ubiquitous Learning

    ERIC Educational Resources Information Center

    Hwang, Gwo-Jen; Kuo, Fan-Ray; Yin, Peng-Yeng; Chuang, Kuo-Hsien

    2010-01-01

    In a context-aware ubiquitous learning environment, learning systems can detect students' learning behaviors in the real-world with the help of context-aware (sensor) technology; that is, students can be guided to observe or operate real-world objects with personalized support from the digital world. In this study, an optimization problem that…

  10. Active Collaborative Learning through Remote Tutoring

    ERIC Educational Resources Information Center

    Gehret, Austin U.; Elliot, Lisa B.; MacDonald, Jonathan H. C.

    2017-01-01

    An exploratory case study approach was used to describe remote tutoring in biochemistry and general chemistry with students who are deaf or hard of hearing (D/HH). Data collected for analysis were based on the observations of the participant tutor. The research questions guiding this study included (1) How is active learning accomplished in…

  11. Active/Cooperative Learning in Schools

    ERIC Educational Resources Information Center

    Bandiera, Milena; Bruno, Costanza

    2006-01-01

    The study describes a teaching action undertaken in the belief that the use of methodologies based on active and cooperative learning could obviate some of the most worrying deficiencies in current scientific teaching, while at the same time supporting the validity of the constructivistic theory that prompted them. A teaching action on genetically…

  12. Accounting for Sustainability: An Active Learning Assignment

    ERIC Educational Resources Information Center

    Gusc, Joanna; van Veen-Dirks, Paula

    2017-01-01

    Purpose: Sustainability is one of the newer topics in the accounting courses taught in university teaching programs. The active learning assignment as described in this paper was developed for use in an accounting course in an undergraduate program. The aim was to enhance teaching about sustainability within such a course. The purpose of this…

  13. Measuring Active Learning to Predict Course Quality

    ERIC Educational Resources Information Center

    Taylor, John E.; Ku, Heng-Yu

    2011-01-01

    This study investigated whether active learning within computer-based training courses can be measured and whether it serves as a predictor of learner-perceived course quality. A major corporation participated in this research, providing access to internal employee training courses, training representatives, and historical course evaluation data.…

  14. Active Learning Strategies and Vocabulary Achievement

    ERIC Educational Resources Information Center

    Griffith, John R.

    2015-01-01

    Using a quantitative method of data collection, this research explored the question: Do active learning strategies used in grades 5 and 6 affect student vocabulary achievement in a positive or negative direction? In their research, Wolfe (2001), Headley, et al., (1995), Freiberg, et al., (1992), and Brunner (2009) emphasize the importance of…

  15. World War II Memorial Learning Activities.

    ERIC Educational Resources Information Center

    Tennessee State Dept. of Education, Nashville.

    These learning activities can help students get the most out of a visit to the Tennessee World War II Memorial, a group of ten pylons located in Nashville (Tennessee). Each pylon contains informational text about the events of World War II. The ten pylons are listed as: (1) "Pylon E-1--Terror: America Enters the War against Fascism, June…

  16. Shock & Anaphylactic Shock. Learning Activity Package.

    ERIC Educational Resources Information Center

    Hime, Kirsten

    This learning activity package on shock and anaphylactic shock is one of a series of 12 titles developed for use in health occupations education programs. Materials in the package include objectives, a list of materials needed, information sheets, reviews (self evaluations) of portions of the content, and answers to reviews. These topics are…

  17. Effects of Review Activities on EFL Learning

    ERIC Educational Resources Information Center

    Lin, Chiu-Lan Nina

    2009-01-01

    The utmost goal of foreign language instruction is aimed at helping the learner master the language. At the same time the learner shall become equipped with linguistic, pragmatic and social-linguistic competence. This study was done to explore if review activities in EFL classes should be mandatory for learners to learn the new knowledge. One…

  18. Live Scale Active Shooter Exercise: Lessons Learned

    ERIC Educational Resources Information Center

    Ervin, Randy

    2008-01-01

    On October 23, 2007, the Lake Land College Public Safety Department conducted a full-scale live exercise that simulated an active shooter and barricaded hostage. In this article, the author will emphasize what they learned, and how they intend to benefit from it. He will list the law enforcement issues and general issues they encountered, and then…

  19. Cashier/Checker Learning Activity Packets (LAPs).

    ERIC Educational Resources Information Center

    Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    Twenty-four learning activity packets (LAPs) are provided for six areas of instruction in a cashier/checker program. Section A, Orientation, contains an LAP on exploring the job of cashier-checker. Section B, Operations, has nine LAPs, including those on operating the cash register, issuing trading stamps, and completing the cash register balance…

  20. Learning Activity Package, Algebra-Trigonometry.

    ERIC Educational Resources Information Center

    Holland, Bill

    A series of ten teacher-prepared Learning Activity Packages (LAPs) in advanced algebra and trigonometry, the units cover logic; absolute value, inequalities, exponents, and complex numbers; functions; higher degree equations and the derivative; the trigonometric function; graphs and applications of the trigonometric functions; sequences and…

  1. Active Citizenship, Education and Service Learning

    ERIC Educational Resources Information Center

    Birdwell, Jonathan; Scott, Ralph; Horley, Edward

    2013-01-01

    This article explores how active citizenship can be encouraged through education and community action. It proposes that service learning and a renewed focus on voluntarism can both promote social cohesion between different ethnic and cultural groups while also fostering among the population a greater understanding of and commitment to civic…

  2. Active Learning Strategies in Physics Teaching

    ERIC Educational Resources Information Center

    Karamustafaoglu, Orhan

    2009-01-01

    The purpose of this study was to determine physics teachers' opinions about student-centered activities applicable in physics teaching and learning in context. A case study approach was used in this research. First, semi-structured interviews were carried out with 6 physics teachers. Then, a questionnaire was developed based on the data obtained…

  3. The Enlightenment Revisited: Sources & Interpretations. Learning Activities.

    ERIC Educational Resources Information Center

    Donato, Clorinda; And Others

    This resource book provides 26 learning activities with background materials for teaching about the Enlightenment. Topics include: (1) "What Was the Enlightenment?"; (2) "An Introduction to the Philosophes"; (3) "Was the Enlightenment a Revolt Against Rationalism?"; (4) "Were the Philosophes Democrats? A…

  4. The Surgical Scrub. Learning Activity Package.

    ERIC Educational Resources Information Center

    Runge, Lillian

    This learning activity package on the surgical scrub is one of a series of 12 titles developed for use in health occupations education programs. Materials in the package include objectives, a list of materials needed, a list of definitions, information sheets, reviews (self evaluations) of portions of the content, and answers to reviews. These…

  5. Active Learning via Student Karaoke Videos

    ERIC Educational Resources Information Center

    Grossman, Gary D.; Richards, Travis

    2016-01-01

    We evaluated students' perceptions and reactions to an active learning Karaoke Video project in both a large (104 student) undergraduate class in Natural History of Georgia and a small graduate seminar in Fish Ecology. Undergraduate responses were evaluated with both questionnaires and triangulation interviews and graduate student responses…

  6. A stochastic learning algorithm for layered neural networks

    SciTech Connect

    Bartlett, E.B.; Uhrig, R.E.

    1992-12-31

    The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.

  7. [Purity Detection Model Update of Maize Seeds Based on Active Learning].

    PubMed

    Tang, Jin-ya; Huang, Min; Zhu, Qi-bing

    2015-08-01

    Seed purity reflects the degree of seed varieties in typical consistent characteristics, so it is great important to improve the reliability and accuracy of seed purity detection to guarantee the quality of seeds. Hyperspectral imaging can reflect the internal and external characteristics of seeds at the same time, which has been widely used in nondestructive detection of agricultural products. The essence of nondestructive detection of agricultural products using hyperspectral imaging technique is to establish the mathematical model between the spectral information and the quality of agricultural products. Since the spectral information is easily affected by the sample growth environment, the stability and generalization of model would weaken when the test samples harvested from different origin and year. Active learning algorithm was investigated to add representative samples to expand the sample space for the original model, so as to implement the rapid update of the model's ability. Random selection (RS) and Kennard-Stone algorithm (KS) were performed to compare the model update effect with active learning algorithm. The experimental results indicated that in the division of different proportion of sample set (1:1, 3:1, 4:1), the updated purity detection model for maize seeds from 2010 year which was added 40 samples selected by active learning algorithm from 2011 year increased the prediction accuracy for 2011 new samples from 47%, 33.75%, 49% to 98.89%, 98.33%, 98.33%. For the updated purity detection model of 2011 year, its prediction accuracy for 2010 new samples increased by 50.83%, 54.58%, 53.75% to 94.57%, 94.02%, 94.57% after adding 56 new samples from 2010 year. Meanwhile the effect of model updated by active learning algorithm was better than that of RS and KS. Therefore, the update for purity detection model of maize seeds is feasible by active learning algorithm.

  8. Comparison of machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

    NASA Astrophysics Data System (ADS)

    Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

    2015-04-01

    Machine learning (ML) algorithms have been successfully evaluated as valuable tools in satellite-based rainfall retrievals which shows the high potential of ML algorithms when faced with high dimensional and complex data. Moreover, the recent developments in parallel computing with ML offer new possibilities in terms of training and predicting speed and therefore makes their usage in real time systems feasible. The present study compares four ML algorithms for rainfall area detection and rainfall rate assignment during daytime, night-time and twilight using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path are applied as predictor variables. As machine learning algorithms, random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) are chosen. The comparison is realised in three steps. First, an extensive tuning study is carried out to customise each of the models. Secondly, the models are trained using the optimum values of model parameters found in the tuning study. Finally, the trained models are used to detect rainfall areas and to assign rainfall rates using an independent validation datasets which is compared against ground-based radar data. To train and validate the models, the radar-based RADOLAN RW product from the German Weather Service (DWD) is used which provides area-wide gauge-adjusted hourly precipitation information. Though the differences in the performance of the algorithms were rather small, NNET and AVNNET have been identified as the most suitable algorithms. On average, they showed the best performance in rainfall area delineation as well as in rainfall rate assignment. The fast computation time of NNET allows to work with large datasets as it is required in remote sensing based rainfall retrievals. However, since none of the algorithms performed considerably better that the others we conclude that research

  9. Convergence analysis of cascade error projection--an efficient learning algorithm for hardware implementation.

    PubMed

    Duong, T A; Stubberud, A R

    2000-06-01

    In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural network. Our analysis also shows that the convergence of the cascade architecture neural network is assured because it satisfies Liapunov criteria, in an added hidden unit domain rather than in the time domain. From this analysis, a mathematical foundation for the cascade correlation learning algorithm can be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case from mathematical analysis in which an efficient hardware learning algorithm called Cascade Error Projection(CEP) is proposed. The CEP provides efficient learning in hardware and it is faster to train, because part of the weights are deterministically obtained, and the learning of the remaining weights from the inputs to the hidden unit is performed as a single-layer perceptron learning with previously determined weights kept frozen. In addition, one can start out with zero weight values (rather than random finite weight values) when the learning of each layer is commenced. Further, unlike cascade correlation algorithm (where a pool of candidate hidden units is added), only a single hidden unit is added at a time. Therefore, the simplicity in hardware implementation is also achieved. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.

  10. An active learning approach to Bloom's Taxonomy.

    PubMed

    Weigel, Fred K; Bonica, Mark

    2014-01-01

    As educators strive toward improving student learning outcomes, many find it difficult to instill their students with a deep understanding of the material the instructors share. One challenge lies in how to provide the material with a meaningful and engaging method that maximizes student understanding and synthesis. By following a simple strategy involving Active Learning across the 3 primary domains of Bloom's Taxonomy (cognitive, affective, and psychomotor), instructors can dramatically improve the quality of the lesson and help students retain and understand the information. By applying our strategy, instructors can engage their students at a deeper level and may even find themselves enjoying the process more.

  11. Cue-based and algorithmic learning in common carp: A possible link to stress coping style.

    PubMed

    Mesquita, Flavia Oliveira; Borcato, Fabio Luiz; Huntingford, Felicity Ann

    2015-06-01

    Common carp that had been screened for stress coping style using a standard behavioural test (response to a novel environment) were given a learning task in which food was concealed in one of two compartments, its location randomised between trials and its presence in a given compartment signalled by either a red or a yellow light. All the fish learned to find food quickly, but did so in different ways. Fifty five percent learned to use the light cue to locate food; the remainder achieved the same result by developing a fixed movement routine. To explore this variation, we related learning strategy to stress coping style. Time to find food fell identically with successive trials in carp classified as reactive or proactive, but reactive fish tended to follow the light cue and proactive fish to adopt a fixed routine. Among fish that learned to follow the light, reactive individuals took fewer trials to reach the learning criterion than did proactive fish. These results add to the growing body of information on within-species variation in learning strategies and suggest a possible influence of stress coping style on the use of associative learning as opposed to algorithmic searching during foraging.

  12. Active Learning Framework Combining Semi-Supervised Approach for Data Stream Mining

    NASA Astrophysics Data System (ADS)

    Kholghi, Mahnoosh; Keyvanpour, Mohammadreza

    In a real stream environment, labeled data may be very scarce and labeling all data is very difficult and expensive. Our goal is to derive a model to predict future instances' label as accurately as possible. Active learning selectively labels instances and can tackle the challenges raised by highly dynamic nature of data stream, but it ignores the effect of unlabeled instances utilization that can help to strength supervised learning. In this paper, we propose a framework that combines active and semi-supervised learning to get advantage of both methods, to boost the performance of learning algorithm. This framework solves the active learning problem in addition to the challenges of evolving data streams. Experimental results on real data sets prove the effectiveness of our proposed framework.

  13. The Validation of the Active Learning in Health Professions Scale

    ERIC Educational Resources Information Center

    Kammer, Rebecca; Schreiner, Laurie; Kim, Young K.; Denial, Aurora

    2015-01-01

    There is a need for an assessment tool for evaluating the effectiveness of active learning strategies such as problem-based learning in promoting deep learning and clinical reasoning skills within the dual environments of didactic and clinical settings in health professions education. The Active Learning in Health Professions Scale (ALPHS)…

  14. Active Learning Environment with Lenses in Geometric Optics

    ERIC Educational Resources Information Center

    Tural, Güner

    2015-01-01

    Geometric optics is one of the difficult topics for students within physics discipline. Students learn better via student-centered active learning environments than the teacher-centered learning environments. So this study aimed to present a guide for middle school teachers to teach lenses in geometric optics via active learning environment…

  15. Detecting anomalies in astronomical signals using machine learning algorithms embedded in an FPGA

    NASA Astrophysics Data System (ADS)

    Saez, Alejandro F.; Herrera, Daniel E.

    2016-07-01

    Taking a large interferometer for radio astronomy, such as the ALMA1 telescope, where the amount of stations (50 in the case of ALMA's main array, which can extend to 64 antennas) produces an enormous amount of data in a short period of time - visibilities can be produced every 16msec or total power information every 1msec (this means up to 2016 baselines). With the aforementioned into account it is becoming more difficult to detect problems in the signal produced by each antenna in a timely manner (one antenna produces 4 x 2GHz spectral windows x 2 polarizations, which means a 16 GHz bandwidth signal which is later digitized using 3-bits samplers). This work will present an approach based on machine learning algorithms for detecting problems in the already digitized signal produced by the active antennas (the set of antennas which is being used in an observation). The aim of this work is to detect unsuitable, or totally corrupted, signals. In addition, this development also provides an almost real time warning which finally helps stop and investigate the problem in order to avoid collecting useless information.

  16. A novel neural-inspired learning algorithm with application to clinical risk prediction.

    PubMed

    Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

    2015-04-01

    Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

  17. Incorporation of Socio-scientific Content into Active Learning Activities

    NASA Astrophysics Data System (ADS)

    King, D. B.; Lewis, J. E.; Anderson, K.; Latch, D.; Sutheimer, S.; Webster, G.; Moog, R.

    2014-12-01

    Active learning has gained increasing support as an effective pedagogical technique to improve student learning. One way to promote active learning in the classroom is the use of in-class activities in place of lecturing. As part of an NSF-funded project, a set of in-class activities have been created that use climate change topics to teach chemistry content. These activities use the Process Oriented Guided Inquiry Learning (POGIL) methodology. In this pedagogical approach a set of models and a series of critical thinking questions are used to guide students through the introduction to or application of course content. Students complete the activities in their groups, with the faculty member as a facilitator of learning. Through assigned group roles and intentionally designed activity structure, process skills, such as teamwork, communication, and information processing, are developed during completion of the activity. Each of these climate change activities contains a socio-scientific component, e.g., social, ethical and economic data. In one activity, greenhouse gases are used to explain the concept of dipole moment. Data about natural and anthropogenic production rates, global warming potential and atmospheric lifetimes for a list of greenhouse gases are presented. The students are asked to identify which greenhouse gas they would regulate, with a corresponding explanation for their choice. They are also asked to identify the disadvantages of regulating the gas they chose in the previous question. In another activity, where carbon sequestration is used to demonstrate the utility of a phase diagram, students use economic and environmental data to choose the best location for sequestration. Too often discussions about climate change (both in and outside the classroom) consist of purely emotional responses. These activities force students to use data to support their arguments and hypothesize about what other data could be used in the corresponding discussion to

  18. Learning deterministic finite automata with a smart state labeling evolutionary algorithm.

    PubMed

    Lucas, Simon M; Reynolds, T Jeff

    2005-07-01

    Learning a Deterministic Finite Automaton (DFA) from a training set of labeled strings is a hard task that has been much studied within the machine learning community. It is equivalent to learning a regular language by example and has applications in language modeling. In this paper, we describe a novel evolutionary method for learning DFA that evolves only the transition matrix and uses a simple deterministic procedure to optimally assign state labels. We compare its performance with the Evidence Driven State Merging (EDSM) algorithm, one of the most powerful known DFA learning algorithms. We present results on random DFA induction problems of varying target size and training set density. We also studythe effects of noisy training data on the evolutionary approach and on EDSM. On noise-free data, we find that our evolutionary method outperforms EDSM on small sparse data sets. In the case of noisy training data, we find that our evolutionary method consistently outperforms EDSM, as well as other significant methods submitted to two recent competitions.

  19. Successful Application of Active Learning Techniques to Introductory Microbiology.

    ERIC Educational Resources Information Center

    Hoffman, Elizabeth A.

    2001-01-01

    Points out the low student achievement in microbiology courses and presents an active learning method applied in an introductory microbiology course which features daily quizzes, cooperative learning activities, and group projects. (Contains 30 references.) (YDS)

  20. Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Salcedo-Sanz, S.; Deo, R. C.; Carro-Calvo, L.; Saavedra-Moreno, B.

    2016-07-01

    Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

  1. A new optical disk driver fine-seek algorithm based on runout learning

    NASA Astrophysics Data System (ADS)

    Chen, Cheng-Hung; Yen, Jia-Yush

    2005-01-01

    The eccentricity in the Optical Disk Drive (ODD) is the inevitable deviation of the geometric center of circular tracks from the rotating center of the disk. The resulted "runout" in the drive is thus periodic with disk rotation. To overcome the runout, conventional approach is for the pick-up head to go forward to the target track while shaking with the period runout during track accessing. This paper proposes an integration of the learning algorithm to learn the runout motion with an on-line observer to estimate the track runout during track accessing. The purpose is to allow for online computation of the target track kinematics so that the controller can adjust the accessing strategy to accommodate for the target track behavior. The proposed algorithm is demonstrated to be feasible through experiments applied to the fine jump control for a general optical storage opto-mechenical-electrical-control plant from OES in ITRI.

  2. A hybrid manifold learning algorithm for the diagnosis and prognostication of Alzheimer's disease.

    PubMed

    Dai, Peng; Gwadry-Sridhar, Femida; Bauer, Michael; Borrie, Michael

    The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. Such data are difficult to compare, visualize, and analyze due to the heterogeneous nature of medical tests. We present a hybrid manifold learning framework, which embeds the feature vectors in a subspace preserving the underlying pairwise similarity structure, i.e. similar/dissimilar pairs. Evaluation tests are carried out using the neuroimaging and biological data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in a three-class (normal, mild cognitive impairment, and AD) classification task using support vector machine (SVM). Furthermore, we make extensive comparison with standard manifold learning algorithms, such as Principal Component Analysis (PCA), Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and isometric feature mapping (Isomap). Experimental results show that our proposed algorithm yields an overall accuracy of 85.33% in the three-class task.

  3. A non-linear camera calibration with modified teaching-learning-based optimization algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Buyang; Yang, Hua; Yang, Shuo

    2015-12-01

    In this paper, we put forward a novel approach based on hierarchical teaching-and-learning-based optimization (HTLBO) algorithm for nonlinear camera calibration. This algorithm simulates the teaching-learning ability of teachers and learners of a classroom. Different from traditional calibration approach, the proposed technique can find the nearoptimal solution without the need of accurate initial parameters estimation (with only very loose parameter bounds). With the introduction of cascade of teaching, the convergence speed is rapid and the global search ability is improved. Results from our study demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness. The HTLBO can also be used to solve many other complex non-linear calibration optimization problems for its good portability.

  4. Bearing fault component identification using information gain and machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Vinay, Vakharia; Kumar, Gupta Vijay; Kumar, Kankar Pavan

    2015-04-01

    In the present study an attempt has been made to identify various bearing faults using machine learning algorithm. Vibration signals obtained from faults in inner race, outer race, rolling element and combined faults are considered. Raw vibration signal cannot be used directly since vibration signals are masked by noise. To overcome this difficulty combined time frequency domain method such as wavelet transform is used. Further wavelet selection criteria based on minimum permutation entropy is employed to select most appropriate base wavelet. Statistical features from selected wavelet coefficients are calculated to form feature vector. To reduce size of feature vector information gain attribute selection method is employed. Modified feature set is fed in to machine learning algorithm such as random forest and self-organizing map for getting maximize fault identification efficiency. Results obtained revealed that attribute selection method shows improvement in fault identification accuracy of bearing components.

  5. A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training.

    PubMed

    Yang, Zhiyong; Zhang, Taohong; Zhang, Dezheng

    2016-02-01

    Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.

  6. Learning algorithm in restricted Boltzmann machines using Kullback-Leibler importance estimation procedure

    NASA Astrophysics Data System (ADS)

    Yasuda, Muneki; Sakurai, Tetsuharu; Tanaka, Kazuyuki

    Restricted Boltzmann machines (RBMs) are bipartite structured statistical neural networks and consist of two layers. One of them is a layer of visible units and the other one is a layer of hidden units. In each layer, any units do not connect to each other. RBMs have high flexibility and rich structure and have been expected to applied to various applications, for example, image and pattern recognitions, face detections and so on. However, most of computational models in RBMs are intractable and often belong to the class of NP-hard problem. In this paper, in order to construct a practical learning algorithm for them, we employ the Kullback-Leibler Importance Estimation Procedure (KLIEP) to RBMs, and give a new scheme of practical approximate learning algorithm for RBMs based on the KLIEP.

  7. Multilabel image classification via high-order label correlation driven active learning.

    PubMed

    Zhang, Bang; Wang, Yang; Chen, Fang

    2014-03-01

    Supervised machine learning techniques have been applied to multilabel image classification problems with tremendous success. Despite disparate learning mechanisms, their performances heavily rely on the quality of training images. However, the acquisition of training images requires significant efforts from human annotators. This hinders the applications of supervised learning techniques to large scale problems. In this paper, we propose a high-order label correlation driven active learning (HoAL) approach that allows the iterative learning algorithm itself to select the informative example-label pairs from which it learns so as to learn an accurate classifier with less annotation efforts. Four crucial issues are considered by the proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to example-label pair; 2) different labels are seldom independent, and label correlations provide critical information for efficient learning; 3) in addition to pair-wise label correlations, high-order label correlations are also informative for multilabel active learning; and 4) since the number of label combinations increases exponentially with respect to the number of labels, an efficient mining method is required to discover informative label correlations. The proposed approach is tested on public data sets, and the empirical results demonstrate its effectiveness.

  8. A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces.

    PubMed

    Sajda, Paul; Gerson, Adam; Müller, Klaus-Robert; Blankertz, Benjamin; Parra, Lucas

    2003-06-01

    We present three datasets that were used to conduct an open competition for evaluating the performance of various machine-learning algorithms used in brain-computer interfaces. The datasets were collected for tasks that included: 1) detecting explicit left/right (L/R) button press; 2) predicting imagined L/R button press; and 3) vertical cursor control. A total of ten entries were submitted to the competition, with winning results reported for two of the three datasets.

  9. Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

    We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions

  10. Active Learning: The Importance of Developing a Comprehensive Measure

    ERIC Educational Resources Information Center

    Carr, Rodney; Palmer, Stuart; Hagel, Pauline

    2015-01-01

    This article reports on an investigation into the validity of a widely used scale for measuring the extent to which higher education students employ active learning strategies. The scale is the active learning scale in the Australasian Survey of Student Engagement. This scale is based on the Active and Collaborative Learning scale of the National…

  11. Reference Framework for Active Learning in Higher Education

    ERIC Educational Resources Information Center

    Naithani, Pranav

    2008-01-01

    The work presented in this paper traces the history of active learning and further utilizes the available literature to define the meaning and importance of active learning in higher education. The study highlights common practical problems faced by students and instructors in implementing active learning in higher education and further identifies…

  12. Changing University Students' Alternative Conceptions of Optics by Active Learning

    ERIC Educational Resources Information Center

    Hadžibegovic, Zalkida; Sliško, Josip

    2013-01-01

    Active learning is individual and group participation in effective activities such as in-class observing, writing, experimenting, discussion, solving problems, and talking about to-be-learned topics. Some instructors believe that active learning is impossible, or at least extremely difficult to achieve in large lecture sessions. Nevertheless, the…

  13. Active Kids Active Minds: A Physical Activity Intervention to Promote Learning?

    ERIC Educational Resources Information Center

    lisahunter; Abbott, Rebecca; Macdonald, Doune; Ziviani, Jennifer; Cuskelly, Monica

    2014-01-01

    This study assessed the feasibility and impact of introducing a programme of an additional 30 minutes per day of moderate physical activity within curriculum time on learning and readiness to learn in a large elementary school in south-east Queensland, Australia. The programme, Active Kids Active Minds (AKAM), involved Year 5 students (n = 107),…

  14. Application of neural networks and other machine learning algorithms to DNA sequence analysis

    SciTech Connect

    Lapedes, A.; Barnes, C.; Burks, C.; Farber, R.; Sirotkin, K.

    1988-01-01

    In this article we report initial, quantitative results on application of simple neutral networks, and simple machine learning methods, to two problems in DNA sequence analysis. The two problems we consider are: (1) determination of whether procaryotic and eucaryotic DNA sequences segments are translated to protein. An accuracy of 99.4% is reported for procaryotic DNA (E. coli) and 98.4% for eucaryotic DNA (H. Sapiens genes known to be expressed in liver); (2) determination of whether eucaryotic DNA sequence segments containing the dinucleotides ''AG'' or ''GT'' are transcribed to RNA splice junctions. Accuracy of 91.2% was achieved on intron/exon splice junctions (acceptor sites) and 92.8% on exon/intron splice junctions (donor sites). The solution of these two problems, by use of information processing algorithms operating on unannotated base sequences and without recourse to biological laboratory work, is relevant to the Human Genome Project. A variety of neural network, machine learning, and information theoretic algorithms are used. The accuracies obtained exceed those of previous investigations for which quantitative results are available in the literature. They result from an ongoing program of research that applies machine learning algorithms to the problem of determining biological function of DNA sequences. Some predictions of possible new genes using these methods are listed -- although a complete survey of the H. sapiens and E. coli sections of GenBank will be given elsewhere. 36 refs., 6 figs., 6 tabs.

  15. A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine †

    PubMed Central

    Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

    2015-01-01

    Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics. PMID:25599427

  16. A fast and precise indoor localization algorithm based on an online sequential extreme learning machine.

    PubMed

    Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

    2015-01-15

    Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

  17. Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

    NASA Astrophysics Data System (ADS)

    Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

    2016-03-01

    Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.

  18. Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes

    PubMed Central

    Borgs, Christian; Chayes, Jennifer T.; Ingrosso, Alessandro; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo

    2016-01-01

    In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations with poor computational performance. Here, we study the difficult case of networks with discrete weights, where the optimization landscape is very rough even for simple architectures, and provide theoretical and numerical evidence of the existence of rare—but extremely dense and accessible—regions of configurations in the network weight space. We define a measure, the robust ensemble (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions. We analytically compute the RE in some exactly solvable models and also provide a general algorithmic scheme that is straightforward to implement: define a cost function given by a sum of a finite number of replicas of the original cost function, with a constraint centering the replicas around a driving assignment. To illustrate this, we derive several powerful algorithms, ranging from Markov Chains to message passing to gradient descent processes, where the algorithms target the robust dense states, resulting in substantial improvements in performance. The weak dependence on the number of precision bits of the weights leads us to conjecture that very similar reasoning applies to more conventional neural networks. Analogous algorithmic schemes can also be applied to other optimization problems. PMID:27856745

  19. A comparison of algorithms for inference and learning in probabilistic graphical models.

    PubMed

    Frey, Brendan J; Jojic, Nebojsa

    2005-09-01

    Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.

  20. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

    PubMed

    Premaladha, J; Ravichandran, K S

    2016-04-01

    Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.

  1. Analysis of image content recognition algorithm based on sparse coding and machine learning

    NASA Astrophysics Data System (ADS)

    Xiao, Yu

    2017-03-01

    This paper presents an image classification algorithm based on spatial sparse coding model and random forest. Firstly, SIFT feature extraction of the image; and then use the sparse encoding theory to generate visual vocabulary based on SIFT features, and using the visual vocabulary of SIFT features into a sparse vector; through the combination of regional integration and spatial sparse vector, the sparse vector gets a fixed dimension is used to represent the image; at last random forest classifier for image sparse vectors for training and testing, using the experimental data set for standard test Caltech-101 and Scene-15. The experimental results show that the proposed algorithm can effectively represent the features of the image and improve the classification accuracy. In this paper, we propose an innovative image recognition algorithm based on image segmentation, sparse coding and multi instance learning. This algorithm introduces the concept of multi instance learning, the image as a multi instance bag, sparse feature transformation by SIFT images as instances, sparse encoding model generation visual vocabulary as the feature space is mapped to the feature space through the statistics on the number of instances in bags, and then use the 1-norm SVM to classify images and generate sample weights to select important image features.

  2. Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

    PubMed

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-04-21

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  3. Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition.

    PubMed

    Stallkamp, J; Schlipsing, M; Salmen, J; Igel, C

    2012-08-01

    Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions and partial occlusions impact the perception of road signs. In practice, a large number of different sign classes needs to be recognized with very high accuracy. Traffic signs have been designed to be easily readable for humans, who perform very well at this task. For computer systems, however, classifying traffic signs still seems to pose a challenging pattern recognition problem. Both image processing and machine learning algorithms are continuously refined to improve on this task. But little systematic comparison of such systems exist. What is the status quo? Do today's algorithms reach human performance? For assessing the performance of state-of-the-art machine learning algorithms, we present a publicly available traffic sign dataset with more than 50,000 images of German road signs in 43 classes. The data was considered in the second stage of the German Traffic Sign Recognition Benchmark held at IJCNN 2011. The results of this competition are reported and the best-performing algorithms are briefly described. Convolutional neural networks (CNNs) showed particularly high classification accuracies in the competition. We measured the performance of human subjects on the same data-and the CNNs outperformed the human test persons.

  4. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    PubMed Central

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-01-01

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

  5. Active machine learning-driven experimentation to determine compound effects on protein patterns.

    PubMed

    Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

    2016-02-03

    High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

  6. Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

    PubMed

    Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin

    2009-10-01

    Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.

  7. Prediction Errors in Learning Drug Response from Gene Expression Data – Influence of Labeling, Sample Size, and Machine Learning Algorithm

    PubMed Central

    Bayer, Immanuel; Groth, Philip; Schneckener, Sebastian

    2013-01-01

    Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response. We compared all possible models for combinations of sample collections, algorithm, drug, and labeling to an identically generated null model. The predictability of treatment effects varies among compounds, i.e. response could be predicted for some but not for all. The choice of sample collection plays a major role towards lowering the prediction error, as does sample size. However, we found that no algorithm was able to consistently outperform the other and there was no significant difference between regression and two- or three class predictors in this experimental setting. These results indicate that response-modeling projects should direct efforts mainly towards sample collection and data quality, rather than method adjustment. PMID:23894636

  8. Prediction errors in learning drug response from gene expression data - influence of labeling, sample size, and machine learning algorithm.

    PubMed

    Bayer, Immanuel; Groth, Philip; Schneckener, Sebastian

    2013-01-01

    Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response. We compared all possible models for combinations of sample collections, algorithm, drug, and labeling to an identically generated null model. The predictability of treatment effects varies among compounds, i.e. response could be predicted for some but not for all. The choice of sample collection plays a major role towards lowering the prediction error, as does sample size. However, we found that no algorithm was able to consistently outperform the other and there was no significant difference between regression and two- or three class predictors in this experimental setting. These results indicate that response-modeling projects should direct efforts mainly towards sample collection and data quality, rather than method adjustment.

  9. Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

    NASA Astrophysics Data System (ADS)

    Nelson, Kevin; Corbin, George; Blowers, Misty

    2014-05-01

    Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

  10. Active Multitask Learning With Trace Norm Regularization Based on Excess Risk.

    PubMed

    Fang, Meng; Yin, Jie; Hall, Lawrence O; Tao, Dacheng

    2016-07-27

    This paper addresses the problem of active learning on multiple tasks, where labeled data are expensive to obtain for each individual task but the learning problems share some commonalities across multiple related tasks. To leverage the benefits of jointly learning from multiple related tasks and making active queries, we propose a novel active multitask learning approach based on trace norm regularized least squares. The basic idea is to induce an optimal classifier which has the lowest risk and at the same time which is closest to the true hypothesis. Toward this aim, we devise a new active selection criterion that takes into account not only the risk but also the excess risk, which measures the distance to the true hypothesis. Based on this criterion, our proposed algorithm actively selects the instance to query for its label based on the combination of the two risks. Experiments on both synthetic and real-world datasets show that our proposed algorithm provides superior performance as compared to other state-of-the-art active learning methods.

  11. Navigating the Active Learning Swamp: Creating an Inviting Environment for Learning.

    ERIC Educational Resources Information Center

    Johnson, Marie C.; Malinowski, Jon C.

    2001-01-01

    Reports on a survey of faculty members (n=29) asking them to define active learning, to rate how effectively different teaching techniques contribute to active learning, and to list the three teaching techniques they use most frequently. Concludes that active learning requires establishing an environment rather than employing a specific teaching…

  12. Notetaking Activity as a Logical Classroom Learning Strategy.

    ERIC Educational Resources Information Center

    Taylor, William; And Others

    The impact on learning performance of a notetaking strategy called the Directed Overt Activity Strategy (DOA) was evaluated on three types of instructional tasks: spatial learning, simple concept learning, and complex concept learning. One hundred volunteer freshman psychology students from Ohio State University used either the DOA or their own…

  13. Predicting Reading and Mathematics from Neural Activity for Feedback Learning

    ERIC Educational Resources Information Center

    Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A.

    2017-01-01

    Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task…

  14. Understanding Fatty Acid Metabolism through an Active Learning Approach

    ERIC Educational Resources Information Center

    Fardilha, M.; Schrader, M.; da Cruz e Silva, O. A. B.; da Cruz e Silva, E. F.

    2010-01-01

    A multi-method active learning approach (MALA) was implemented in the Medical Biochemistry teaching unit of the Biomedical Sciences degree at the University of Aveiro, using problem-based learning as the main learning approach. In this type of learning strategy, students are involved beyond the mere exercise of being taught by listening. Less…

  15. Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization.

    PubMed

    Wang, Jie-sheng; Li, Shu-xia; Song, Jiang-di

    2015-01-01

    In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

  16. Active Learning With Optimal Instance Subset Selection.

    PubMed

    Fu, Yifan; Zhu, Xingquan; Elmagarmid, A K

    2013-04-01

    Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannot produce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and individuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieve AL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achieve the goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity between instances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to select a k-instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-art approaches for AL.

  17. Definition and Analysis of a System for the Automated Comparison of Curriculum Sequencing Algorithms in Adaptive Distance Learning

    ERIC Educational Resources Information Center

    Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia

    2011-01-01

    LS-Lab provides automatic support to comparison/evaluation of the Learning Object Sequences produced by different Curriculum Sequencing Algorithms. Through this framework a teacher can verify the correspondence between the behaviour of different sequencing algorithms and her pedagogical preferences. In fact the teacher can compare algorithms…

  18. Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

    PubMed

    Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent

    2014-02-01

    When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most

  19. A Bridge to Active Learning: A Summer Bridge Program Helps Students Maximize Their Active-Learning Experiences and the Active-Learning Experiences of Others

    PubMed Central

    Cooper, Katelyn M.; Ashley, Michael; Brownell, Sara E.

    2017-01-01

    National calls to improve student academic success in college have sparked the development of bridge programs designed to help students transition from high school to college. We designed a 2-week Summer Bridge program that taught introductory biology content in an active-learning way. Through a set of exploratory interviews, we unexpectedly identified that Bridge students had developed sophisticated views of active learning, even though this was not an explicit goal of the program. We conducted an additional set of semistructured interviews that focused on active learning and compared the interviews of Bridge students with those from non-Bridge students who had been eligible for but did not participate in the program. We used the constant comparative method to identify themes from the interviews. We found that Bridge students perceived that, because they knew how to approach active learning and viewed it as important, they benefited more from active learning in introductory biology than non-Bridge students. Specifically, Bridge students seemed to be more aware of their own learning gains from participating in active learning. Compared with the majority of non-Bridge students, the majority of Bridge students described using a greater variety of strategies to maximize their experiences in active learning. Finally, in contrast to non-Bridge students, Bridge students indicated that they take an equitable approach to group work. These findings suggest that we may be able to prime students to maximize their own and other’s experiences in active learning. PMID:28232588

  20. A Bridge to Active Learning: A Summer Bridge Program Helps Students Maximize Their Active-Learning Experiences and the Active-Learning Experiences of Others.

    PubMed

    Cooper, Katelyn M; Ashley, Michael; Brownell, Sara E

    2017-01-01

    National calls to improve student academic success in college have sparked the development of bridge programs designed to help students transition from high school to college. We designed a 2-week Summer Bridge program that taught introductory biology content in an active-learning way. Through a set of exploratory interviews, we unexpectedly identified that Bridge students had developed sophisticated views of active learning, even though this was not an explicit goal of the program. We conducted an additional set of semistructured interviews that focused on active learning and compared the interviews of Bridge students with those from non-Bridge students who had been eligible for but did not participate in the program. We used the constant comparative method to identify themes from the interviews. We found that Bridge students perceived that, because they knew how to approach active learning and viewed it as important, they benefited more from active learning in introductory biology than non-Bridge students. Specifically, Bridge students seemed to be more aware of their own learning gains from participating in active learning. Compared with the majority of non-Bridge students, the majority of Bridge students described using a greater variety of strategies to maximize their experiences in active learning. Finally, in contrast to non-Bridge students, Bridge students indicated that they take an equitable approach to group work. These findings suggest that we may be able to prime students to maximize their own and other's experiences in active learning.

  1. Strategies for active learning in online continuing education.

    PubMed

    Phillips, Janet M

    2005-01-01

    Online continuing education and staff development is on the rise as the benefits of access, convenience, and quality learning are continuing to take shape. Strategies to enhance learning call for learner participation that is self-directed and independent, thus changing the educator's role from expert to coach and facilitator. Good planning of active learning strategies promotes optimal learning whether the learning content is presented in a course or a just-in-time short module. Active learning strategies can be used to enhance online learning during all phases of the teaching-learning process and can accommodate a variety of learning styles. Feedback from peers, educators, and technology greatly influences learner satisfaction and must be harnessed to provide effective learning experiences. Outcomes of active learning can be assessed online and implemented conveniently and successfully from the initiation of the course or module planning to the end of the evaluation process. Online learning has become accessible and convenient and allows the educator to track learner participation. The future of online education will continue to grow, and using active learning strategies will ensure that quality learning will occur, appealing to a wide variety of learning needs.

  2. Active vibration control of smart composite plates using LQR algorithm

    NASA Astrophysics Data System (ADS)

    Suresh, R.; Venkateshwara Rao, G.

    2003-10-01

    The concept of using the actuators and sensors to form a self controlling and self monitoring smart system in advanced structural design has drawn considerable interest among the research community. The smart system has large number of active, light weight, distributed sensors and actuators either bonded or embedded in the structure for the purpose of vibration suppression, shape and acoustic controls as well as fault detection and mitigation. The present study addresses the issues related to the active vibration control schemes for the smart composite panels, with substrate as the fiber reinforced composite laminate and the piezo ceramic layers as the actuators and sensors, using LQR algorithm. The study involves the structural modelling, controller design, open and closed loop system response analysis. For this purpose, an eight noded isoparametric finite element with seven degrees of freedom, viz., three translations, two section rotations and two potential differences corresponding to the actuators and sensors is developed. The piezo-ceramic actuator and sensor layers are also considered as the load bearing components in the panel. The finite element equations are first transformed into the modal state space form and then are used to obtain the constant controller gains. These are used to obtain the closed loop responses.

  3. STEM learning activity among home-educating families

    NASA Astrophysics Data System (ADS)

    Bachman, Jennifer

    2011-12-01

    Science, technology, engineering, and mathematics (STEM) learning was studied among families in a group of home-educators in the Pacific Northwest. Ethnographic methods recorded learning activity (video, audio, fieldnotes, and artifacts) which was analyzed using a unique combination of Cultural-Historical Activity Theory (CHAT) and Mediated Action (MA), enabling analysis of activity at multiple levels. Findings indicate that STEM learning activity is family-led, guided by parents' values and goals for learning, and negotiated with children to account for learner interests and differences, and available resources. Families' STEM education practice is dynamic, evolves, and influenced by larger societal STEM learning activity. Parents actively seek support and resources for STEM learning within their home-school community, working individually and collectively to share their funds of knowledge. Home-schoolers also access a wide variety of free-choice learning resources: web-based materials, museums, libraries, and community education opportunities (e.g. afterschool, weekend and summer programs, science clubs and classes, etc.). A lesson-heuristic, grounded in Mediated Action, represents and analyzes home STEM learning activity in terms of tensions between parental goals, roles, and lesson structure. One tension observed was between 'academic' goals or school-like activity and 'lifelong' goals or everyday learning activity. Theoretical and experiential learning was found in both activity, though parents with academic goals tended to focus more on theoretical learning and those with lifelong learning goals tended to be more experiential. Examples of the National Research Council's science learning strands (NRC, 2009) were observed in the STEM practices of all these families. Findings contribute to the small but growing body of empirical CHAT research in science education, specifically to the empirical base of family STEM learning practices at home. It also fills a

  4. Effects of Sharing Clickers in an Active Learning Environment

    ERIC Educational Resources Information Center

    Daniel, Todd; Tivener, Kristin

    2016-01-01

    Scientific research into learning enhancement gained by the use of clickers in active classrooms has largely focused on the use of individual clickers. In this study, we compared the learning experiences of participants in active learning groups in which an entire small group shared a single clicker to groups in which each member of the group had…

  5. Clickers in the Classroom: An Active Learning Approach

    ERIC Educational Resources Information Center

    Martyn, Margie

    2007-01-01

    Current research describes the benefits of active learning approaches. Clickers, or student response systems, are a technology used to promoted active learning. Most research on the benefits of using clickers in the classroom has shown that students become engaged and enjoy using them. However, research on learning outcomes has only compared the…

  6. Silent Students' Participation in a Large Active Learning Science Classroom

    ERIC Educational Resources Information Center

    Obenland, Carrie A.; Munson, Ashlyn H.; Hutchinson, John S.

    2012-01-01

    Active learning in large science classrooms furthers opportunities for students to engage in the content and in meaningful learning, yet students can still remain anonymously silent. This study aims to understand the impact of active learning on these silent students in a large General Chemistry course taught via Socratic questioning and…

  7. Integrating Active Learning and Assessment in the Accounting Classroom

    ERIC Educational Resources Information Center

    Carter, Fonda L.; Hogan, Patrick T.

    2013-01-01

    Some colleges and universities are utilizing the inclusion of more active learning techniques in course content. Active learning involves students in thinking about what they are doing as they accomplish tasks or assignments in order to develop a deeper understanding of the topic or issue. In addition to a focus on enhancing student learning, the…

  8. Opportunities to Create Active Learning Techniques in the Classroom

    ERIC Educational Resources Information Center

    Camacho, Danielle J.; Legare, Jill M.

    2015-01-01

    The purpose of this article is to contribute to the growing body of research that focuses on active learning techniques. Active learning techniques require students to consider a given set of information, analyze, process, and prepare to restate what has been learned--all strategies are confirmed to improve higher order thinking skills. Active…

  9. Teacher Educators' Design and Implementation of Group Learning Activities

    ERIC Educational Resources Information Center

    De Hei, Miranda S. A.; Sjoer, Ellen; Admiraal, Wilfried; Strijbos, Jan-Willem

    2016-01-01

    The aim of this study was to describe how teacher educators design and implement group learning activities (GLAs). We used the Group Learning Activities Instructional Design (GLAID) framework to analyse their descriptions. The GLAID framework includes eight components: (1) interaction, (2) learning objectives and outcomes, (3) assessment, (4) task…

  10. Incorporating Active Learning Techniques into a Genetics Class

    ERIC Educational Resources Information Center

    Lee, W. Theodore; Jabot, Michael E.

    2011-01-01

    We revised a sophomore-level genetics class to more actively engage the students in their learning. The students worked in groups on quizzes using the Immediate Feedback Assessment Technique (IF-AT) and active-learning projects. The IF-AT quizzes allowed students to discuss key concepts in small groups and learn the correct answers in class. The…

  11. Contemplating a Constructivist Stance for Active Learning within Music Education

    ERIC Educational Resources Information Center

    Scott, Sheila

    2011-01-01

    This article examines constructivist philosophies for learning with an emphasis on student-centered environments in education and the active involvement of students in learning as they relate new understanding to what they already know and refine previous skills in terms of newly acquired proficiencies. Active learning is explored from a…

  12. Enhancing Learning Outcomes through Application Driven Activities in Marketing

    ERIC Educational Resources Information Center

    Stegemann, Nicole; Sutton-Brady, Catherine

    2013-01-01

    This paper introduces an activity used in class to allow students to apply previously acquired information to a hands-on task. As the authors have previously shown active learning is a way to effectively facilitate and improve students' learning outcomes. As a result to improve learning outcomes we have overtime developed a series of learning…

  13. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    PubMed

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

  14. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  15. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

    DOE PAGES

    Ling, Julia; Templeton, Jeremy Alan

    2015-08-04

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests.more » The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.« less

  16. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

    SciTech Connect

    Ling, Julia; Templeton, Jeremy Alan

    2015-08-04

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

  17. FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks.

    PubMed

    Zhang, Zhen; Zhao, Dongbin; Gao, Junwei; Wang, Dongqing; Dai, Yujie

    2016-04-14

    In this paper, we propose a multiagent reinforcement learning algorithm dealing with fully cooperative tasks. The algorithm is called frequency of the maximum reward Q-learning (FMRQ). FMRQ aims to achieve one of the optimal Nash equilibria so as to optimize the performance index in multiagent systems. The frequency of obtaining the highest global immediate reward instead of immediate reward is used as the reinforcement signal. With FMRQ each agent does not need the observation of the other agents' actions and only shares its state and reward at each step. We validate FMRQ through case studies of repeated games: four cases of two-player two-action and one case of three-player two-action. It is demonstrated that FMRQ can converge to one of the optimal Nash equilibria in these cases. Moreover, comparison experiments on tasks with multiple states and finite steps are conducted. One is box-pushing and the other one is distributed sensor network problem. Experimental results show that the proposed algorithm outperforms others with higher performance.

  18. Feasibility of the MUSIC Algorithm for the Active Protection System

    DTIC Science & Technology

    2001-03-01

    methods of computing the doppler frequency the multiple signal classification ( MUSIC ) algorithm and power spectral density (PSD) with the use of fast...Fourier transform (1024-point FFT). Normally, MUSIC has been used to improve the resolution of multiple closely spaced targets. In this application, MUSIC ...assumed head-on projectile using FSD and the MUSIC algorithm; I wanted to determine whether the MUSIC algorithm performs better than PSD in terms of accuracy and processing time.

  19. Classification and authentication of unknown water samples using machine learning algorithms.

    PubMed

    Kundu, Palash K; Panchariya, P C; Kundu, Madhusree

    2011-07-01

    This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples.

  20. Getting To Know You: Activities for Learning Names.

    ERIC Educational Resources Information Center

    Jordan, Debra J.

    1998-01-01

    Learning names is vital to the enjoyment and productivity of a group. Presents four games to help campers learn each others' names. Sidebar presents three additional teambuilding activities and ice breakers. (TD)

  1. Active-Learning versus Teacher-Centered Instruction for Learning Acids and Bases

    ERIC Educational Resources Information Center

    Sesen, Burcin Acar; Tarhan, Leman

    2011-01-01

    Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of "acids and bases". Sample: The sample of this…

  2. Patterns of Field Learning Activities and Their Relation to Learning Outcome

    ERIC Educational Resources Information Center

    Lee, Mingun; Fortune, Anne E.

    2013-01-01

    Field practicum is an active learning process. This study explores the different learning stages or processes students experience during their field practicum. First-year master's of social work students in field practica were asked how much they had engaged in educational learning activities such as observation, working independently, process…

  3. A New Surrogate-Assisted Interactive Genetic Algorithm With Weighted Semisupervised Learning.

    PubMed

    Sun, Xiaoyan; Gong, Dunwei; Jin, Yaochu; Chen, Shanshan

    2013-04-01

    Surrogate-assisted interactive genetic algorithms (IGAs) are found to be very effective in reducing human fatigue. Different from models used in most surrogate-assisted evolutionary algorithms, surrogates in IGA must be able to handle the inherent uncertainties in fitness assignment by human users, where, e.g., interval-based fitness values are assigned to individuals. This poses another challenge to using surrogates for fitness approximation in evolutionary optimization, in addition to the lack of training data. In this paper, a new surrogate-assisted IGA has been proposed, where the uncertainty in subjective fitness evaluations is exploited both in training the surrogates and in managing surrogates. To enhance the approximation accuracy of the surrogates, an improved cotraining algorithm for semisupervised learning has been suggested, where the uncertainty in interval-based fitness values is taken into account in training and weighting the two cotrained models. Moreover, uncertainty in the interval-based fitness values is also considered in model management so that not only the best individuals but also the most uncertain individuals will be chosen to be re-evaluated by the human user. The effectiveness of the proposed algorithm is verified on two test problems as well as in fashion design, a typical application of IGA. Our results indicate that the new surrogate-assisted IGA can effectively alleviate user fatigue and is more likely to find acceptable solutions in solving complex design problems.

  4. Universal perceptron and DNA-like learning algorithm for binary neural networks: non-LSBF implementation.

    PubMed

    Chen, Fangyue; Chen, Guanrong; He, Qinbin; He, Guolong; Xu, Xiubin

    2009-08-01

    Implementing linearly nonseparable Boolean functions (non-LSBF) has been an important and yet challenging task due to the extremely high complexity of this kind of functions and the exponentially increasing percentage of the number of non-LSBF in the entire set of Boolean functions as the number of input variables increases. In this paper, an algorithm named DNA-like learning and decomposing algorithm (DNA-like LDA) is proposed, which is capable of effectively implementing non-LSBF. The novel algorithm first trains the DNA-like offset sequence and decomposes non-LSBF into logic XOR operations of a sequence of LSBF, and then determines the weight-threshold values of the multilayer perceptron (MLP) that perform both the decompositions of LSBF and the function mapping the hidden neurons to the output neuron. The algorithm is validated by two typical examples about the problem of approximating the circular region and the well-known n-bit parity Boolean function (PBF).

  5. Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Negro Maggio, Valentina; Iocchi, Luca

    2015-02-01

    Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

  6. Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

    PubMed Central

    Maenner, Matthew J.; Yeargin-Allsopp, Marshalyn; Van Naarden Braun, Kim; Christensen, Deborah L.; Schieve, Laura A.

    2016-01-01

    The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children’s developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods). The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria). We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive). The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD “prevalence” was 1.46% compared to the published (clinician-determined) estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site. PMID:28002438

  7. Activity Learning and Learning Activity: Discussions of a Concept, and an Outline for an Empirical Study.

    ERIC Educational Resources Information Center

    Hallden, Ola

    This paper is a first report from the project "Activity Learning and Cooperation," financed by the Swedish Board of Education. The aim of the project is to establish a theoretical basis for a field study of locally initiated experiments using various teaching strategies. More specifically, this paper is restricted to a discussion of the…

  8. Is Active Learning Like Broccoli? Student Perceptions of Active Learning in Large Lecture Classes

    ERIC Educational Resources Information Center

    Smith, C. Veronica; Cardaciotto, LeeAnn

    2011-01-01

    Although research suggests that active learning is associated with positive outcomes (e.g., memory, test performance), use of such techniques can be difficult to implement in large lecture-based classes. In the current study, 1,091 students completed out-of-class group exercises to complement course material in an Introductory Psychology class.…

  9. "Heart Shots": a classroom activity to instigate active learning.

    PubMed

    Abraham, Reem Rachel; Vashe, Asha; Torke, Sharmila

    2015-09-01

    The present study aimed to provide undergraduate medical students at Melaka Manipal Medical College (Manipal Campus), Manipal University, in Karnataka, India, an opportunity to apply their knowledge in cardiovascular concepts to real-life situations. A group activity named "Heart Shots" was implemented for a batch of first-year undergraduate students (n = 105) at the end of a block (teaching unit). Students were divided into 10 groups each having 10-11 students. They were requested to make a video/PowerPoint presentation about the application of cardiovascular principles to real-life situations. The presentation was required to be of only pictures/photos and no text material, with a maximum duration of 7 min. More than 95% of students considered that the activity helped them to apply their knowledge in cardiovascular concepts to real-life situations and understand the relevance of physiology in medicine and to revise the topic. More than 90% of students agreed that the activity helped them to apply their creativity in improving their knowledge and to establish a link between concepts rather than learning them as isolated facts. Based on the feedback, we conclude that the activity was student centered and that it facilitated learning.

  10. On-line and Mobil Learning Activities

    NASA Astrophysics Data System (ADS)

    Ackerman, S. A.; Whittaker, T. M.; Jasmin, T.; Mooney, M. E.

    2012-12-01

    Introductory college-level science courses for non-majors are critical gateways to imparting not only discipline-specific information, but also the basics of the scientific method and how science influences society. They are also indispensable for student success to degree. On-line, web-based homework (whether on computers or mobile devices) is a rapidly growing use of the Internet and is becoming a major component of instruction in science, replacing delayed feedback from a few major exams. Web delivery and grading of traditional textbook-type questions is equally effective as having students write them out for hand grading, as measured by student performance on conceptual and problem solving exams. During this presentation we will demonstrate some of the interactive on-line activities used to teach concepts and how scientists approach problem solving, and how these activities have impacted student learning. Evaluation of the activities, including formative and summative, will be discussed and provide evidence that these interactive activities significantly enhance understanding of introductory meteorological concepts in a college-level science course. More advanced interactive activities are also used in our courses for department majors, some of these will be discussed and demonstrated. Bring your mobile devices to play along! Here is an example on teaching contouring: http://profhorn.aos.wisc.edu/wxwise/contour/index.html

  11. A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

    PubMed

    Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

    2014-01-01

    To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

  12. A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

    PubMed Central

    Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

    2014-01-01

    To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems. PMID:24574905

  13. A framework for porting the NeuroBayes machine learning algorithm to FPGAs

    NASA Astrophysics Data System (ADS)

    Baehr, S.; Sander, O.; Heck, M.; Feindt, M.; Becker, J.

    2016-01-01

    The NeuroBayes machine learning algorithm is deployed for online data reduction at the pixel detector of Belle II. In order to test, characterize and easily adapt its implementation on FPGAs, a framework was developed. Within the framework an HDL model, written in python using MyHDL, is used for fast exploration of possible configurations. Under usage of input data from physics simulations figures of merit like throughput, accuracy and resource demand of the implementation are evaluated in a fast and flexible way. Functional validation is supported by usage of unit tests and HDL simulation for chosen configurations.

  14. Implementation of a new iterative learning control algorithm on real data

    NASA Astrophysics Data System (ADS)

    Zamanian, Hamed; Koohi, Ardavan

    2016-02-01

    In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome.

  15. How to learn effectively in medical school: test yourself, learn actively, and repeat in intervals.

    PubMed

    Augustin, Marc

    2014-06-01

    Students in medical school often feel overwhelmed by the excessive amount of factual knowledge they are obliged to learn. Although a large body of research on effective learning methods is published, scientifically based learning strategies are not a standard part of the curriculum in medical school. Students are largely unaware of how to learn successfully and improve memory. This review outlines three fundamental methods that benefit learning: the testing effect, active recall, and spaced repetition. The review summarizes practical learning strategies to learn effectively and optimize long-term retention of factual knowledge.

  16. A Supplier Bidding Strategy Through Q-Learning Algorithm in Electricity Auction Markets

    NASA Astrophysics Data System (ADS)

    Xiong, Gaofeng; Hashiyama, Tomonori; Okuma, Shigeru

    One of the most important issues for power suppliers in the deregulated electric industry is how to bid into the electricity auction market to satisfy their profit-maximizing goals. Based on the Q-Learning algorithm, this paper presents a novel supplier bidding strategy to maximize supplier’s profit in the long run. In this approach, the supplier bidding strategy is viewed as a kind of stochastic optimal control problem and each supplier can learn from experience. A competitive day-ahead electricity auction market with hourly bids is assumed here, where no supplier possesses the market power. The dynamics and the incomplete information of the market are considered. The impacts of suppliers’ strategic bidding on the market price are analyzed under uniform pricing rule and discriminatory pricing rule. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.

  17. PEDLA: predicting enhancers with a deep learning-based algorithmic framework

    PubMed Central

    Liu, Feng; Li, Hao; Ren, Chao; Bo, Xiaochen; Shu, Wenjie

    2016-01-01

    Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues. PMID:27329130

  18. PEDLA: predicting enhancers with a deep learning-based algorithmic framework.

    PubMed

    Liu, Feng; Li, Hao; Ren, Chao; Bo, Xiaochen; Shu, Wenjie

    2016-06-22

    Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.

  19. Meeting "Learned Helplessness" Head on with "Active Learning."

    ERIC Educational Resources Information Center

    Spence, Ian; Stan-Spence, Aileen

    Learned helplessness is an insidious condition involving undeveloped executive functioning, lack of persistence, and an undeveloped sense of connecting new words or concepts into a web of meanings. Remedial teaching in most small-group, diagnostic/prescriptive settings encourages continued learned helplessness because students are dependent on the…

  20. Active Learning Strategies and Assessment in World Geography Classes

    ERIC Educational Resources Information Center

    Klein, Phil

    2003-01-01

    Active learning strategies include a variety of methods, such as inquiry and discovery, in which students are actively engaged in the learning process. This article describes several strategies that can be used in secondary-or college-level world geography courses. The goal of these activities is to foster development of a spatial perspective in…

  1. Is Peer Interaction Necessary for Optimal Active Learning?

    PubMed Central

    Farmer, Jan Keith; Peterson, Ernie

    2014-01-01

    Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of inexperience, we should try to provide more explicit implementation recommendations based on research into the key components of effective active learning. We investigated the optimal implementation of active-learning exercises within a “lecture” course. Two sections of nonmajors biology were taught by the same instructor, in the same semester, using the same instructional materials and assessments. Students in one section completed in-class active-learning exercises in cooperative groups, while students in the other section completed the same activities individually. Performance on low-level, multiple-choice assessments was not significantly different between sections. However, students who worked in cooperative groups on the in-class activities significantly outperformed students who completed the activities individually on the higher-level, extended-response questions. Our results provide additional evidence that group processing of activities should be the recommended mode of implementation for in-class active-learning exercises. PMID:26086656

  2. Is Peer Interaction Necessary for Optimal Active Learning?

    PubMed

    Linton, Debra L; Farmer, Jan Keith; Peterson, Ernie

    2014-01-01

    Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of inexperience, we should try to provide more explicit implementation recommendations based on research into the key components of effective active learning. We investigated the optimal implementation of active-learning exercises within a "lecture" course. Two sections of nonmajors biology were taught by the same instructor, in the same semester, using the same instructional materials and assessments. Students in one section completed in-class active-learning exercises in cooperative groups, while students in the other section completed the same activities individually. Performance on low-level, multiple-choice assessments was not significantly different between sections. However, students who worked in cooperative groups on the in-class activities significantly outperformed students who completed the activities individually on the higher-level, extended-response questions. Our results provide additional evidence that group processing of activities should be the recommended mode of implementation for in-class active-learning exercises.

  3. An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

    NASA Astrophysics Data System (ADS)

    Balasubramanian, R.

    2007-12-01

    Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

  4. Innate Visual Learning through Spontaneous Activity Patterns

    PubMed Central

    Albert, Mark V.; Schnabel, Adam; Field, David J.

    2008-01-01

    Patterns of spontaneous activity in the developing retina, LGN, and cortex are necessary for the proper development of visual cortex. With these patterns intact, the primary visual cortices of many newborn animals develop properties similar to those of the adult cortex but without the training benefit of visual experience. Previous models have demonstrated how V1 responses can be initialized through mechanisms specific to development and prior to visual experience, such as using axonal guidance cues or relying on simple, pairwise correlations on spontaneous activity with additional developmental constraints. We argue that these spontaneous patterns may be better understood as part of an “innate learning” strategy, which learns similarly on activity both before and during visual experience. With an abstraction of spontaneous activity models, we show how the visual system may be able to bootstrap an efficient code for its natural environment prior to external visual experience, and we continue the same refinement strategy upon natural experience. The patterns are generated through simple, local interactions and contain the same relevant statistical properties of retinal waves and hypothesized waves in the LGN and V1. An efficient encoding of these patterns resembles a sparse coding of natural images by producing neurons with localized, oriented, bandpass structure—the same code found in early visual cortical cells. We address the relevance of higher-order statistical properties of spontaneous activity, how this relates to a system that may adapt similarly on activity prior to and during natural experience, and how these concepts ultimately relate to an efficient coding of our natural world. PMID:18670593

  5. Perception towards Mobile Learning Activities among Post Graduate Students

    ERIC Educational Resources Information Center

    Thiyagu, K.

    2012-01-01

    M-learning is learning supported by mobile devices and intelligent user interfaces. Compared to the prior generation a few years ago, storage capacity and screen size of mobile devices as well as transfer speed of wireless connections have significantly increased. Equipped with mobile devices, learners can conduct learning activities at anytime…

  6. Performance in Physiology Evaluation: Possible Improvement by Active Learning Strategies

    ERIC Educational Resources Information Center

    Montrezor, Luís H.

    2016-01-01

    The evaluation process is complex and extremely important in the teaching/learning process. Evaluations are constantly employed in the classroom to assist students in the learning process and to help teachers improve the teaching process. The use of active methodologies encourages students to participate in the learning process, encourages…

  7. Active Learning by Play Dough Modeling in the Medical Profession

    ERIC Educational Resources Information Center

    Herur, Anita; Kolagi, Sanjeev; Chinagudi, Surekharani; Manjula, R.; Patil, Shailaja

    2011-01-01

    Active learning produces meaningful learning, improves attitudes toward learning, and increases knowledge and retention, but is still not fully institutionalized in the undergraduate sciences. A few studies have compared the effectiveness of PowerPoint presentations, student seminars, quizzes, and use of CD-ROMs with blackboard teaching and…

  8. Individualized Instruction in Science, Earth Space Project, Learning Activities Package.

    ERIC Educational Resources Information Center

    Kuczma, R. M.

    Learning Activity Packages (LAP) relating to the earth and space are presented for use in sampling a new type of learning for a whole year. Eighteen topics are incorporated into five units: (1) introduction to individualized learning, (2) observation versus interpretation, (3) chemistry in the space age, (4) the space age interdisciplines, and (5)…

  9. CurioCity, Developing an "Active Learning" Game.

    ERIC Educational Resources Information Center

    Ferguson, Lynne

    1999-01-01

    Describes a case study that takes readers through a human-centered design process used in developing an "Active Learning" tool, CurioCity, a game for students in grades 7-10. Attempts to better understand multiculturalism and to bridge formal in-school learning with informal field trip learning. (SC)

  10. Teacher Feedback during Active Learning: Current Practices in Primary Schools

    ERIC Educational Resources Information Center

    van den Bergh, Linda; Ros, Anje; Beijaard, Douwe

    2013-01-01

    Background: Feedback is one of the most powerful tools, which teachers can use to enhance student learning. It appears dif?cult for teachers to give qualitatively good feedback, especially during active learning. In this context, teachers should provide facilitative feedback that is focused on the development of meta-cognition and social learning.…

  11. Two kinds of active impulsive noise control algorithms based on sigmoid transformation

    NASA Astrophysics Data System (ADS)

    Li, Pei; Bai, Xuefeng; Ma, Yongjian

    2017-01-01

    In this thesis, active noise control of symmetric α stable (SαS) distribution impulsive noise has been studied. Two kinds of algorithm based on Sigmoid transformation of error signal have been proposed. The convergence condition of algorithms also has been analyzed. It does not need the parameter selection and thresholds estimation. Computer simulations were carried out to validate algorithm. Simulation results have proven the effectiveness of the algorithm and achieved the expected control effect. Compared to the previous algorithm, the convergence speed is improved.

  12. Oxalate Blockage of Calcium and Iron: A Student Learning Activity.

    ERIC Educational Resources Information Center

    Walker, Noojin

    1988-01-01

    Describes a student learning activity used to teach the meaning of percentage composition, mole concept, selective precipitation, and limiting factors. Presents two word problems and their solutions. (CW)

  13. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    PubMed Central

    Pizarro, Ricardo A.; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A.; Goldman, Aaron L.; Xiao, Ena; Luo, Qian; Berman, Karen F.; Callicott, Joseph H.; Weinberger, Daniel R.; Mattay, Venkata S.

    2016-01-01

    High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI. PMID:28066227

  14. A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

    PubMed

    Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu

    2007-01-01

    This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

  15. Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms

    PubMed Central

    Yau, Kok-Lim Alvin; Poh, Geong-Sen; Chien, Su Fong; Al-Rawi, Hasan A. A.

    2014-01-01

    Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR. PMID:24995352

  16. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

    PubMed

    Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

    2016-01-01

    High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  17. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

    PubMed

    Hatipoglu, Nuh; Bilgin, Gokhan

    2017-02-28

    In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

  18. Processing of rock core microtomography images: Using seven different machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Chauhan, Swarup; Rühaak, Wolfram; Khan, Faisal; Enzmann, Frieder; Mielke, Philipp; Kersten, Michael; Sass, Ingo

    2016-01-01

    The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore size distribution was found to be strongly affected by the feature vectors selected. Relative porosity average value of 15.92±1.77% retrieved from all the seven machine learning algorithm is in very good agreement with the experimental results of 17±2%, obtained using gas pycnometer. Of the supervised techniques, the least square support vector machine technique is superior to feed forward artificial neural network because of its ability to identify a generalized pattern. In the ensemble classification techniques boosting technique converged faster compared to bragging technique. The k-means technique outperformed the fuzzy c-means and self-organized maps techniques in terms of accuracy and speed.

  19. A measurement fusion method for nonlinear system identification using a cooperative learning algorithm.

    PubMed

    Xia, Youshen; Kamel, Mohamed S

    2007-06-01

    Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.

  20. Using active learning strategies to present bloodborne pathogen programs.

    PubMed

    Weaver, Mary G

    2003-06-01

    Every year, school nurses have the responsibility for developing and presenting a bloodborne pathogen presentation to the education and clerical staff of their buildings. Although the information is similar from year to year, the manner in which the information is presented can be altered. Teachers are using active learning strategies in a variety of learning environments, engaging students in the learning process by having them play an active role. With some planning, preparation, and imagination, active learning strategies can be incorporated into bloodborne pathogen presentations. The purpose of this article is to define active learning, describe how to develop a program using active learning strategies, and provide some examples of bloodborne pathogen presentations that have already been developed. Several sources are identified that can provide the school nurse with information regarding bloodborne pathogens. Information about how computers can be integrated into the bloodborne pathogen presentation is also presented.

  1. Statistical Learning Algorithm for In-situ and Invasive Breast Carcinoma Segmentation

    PubMed Central

    Jayender, Jagadeesan; Gombos, Eva; Chikarmane, Sona; Dabydeen, Donnette; Jolesz, Ferenc A.; Vosburgh, Kirby G.

    2013-01-01

    DCE-MRI has proven to be a highly sensitive imaging modality in diagnosing breast cancers. However, analyzing the DCE-MRI is time-consuming and prone to errors due to the large volume of data. Mathematical models to quantify contrast perfusion, such as the Black Box methods and Pharmacokinetic analysis, are inaccurate, sensitive to noise and depend on a large number of external factors such as imaging parameters, patient physiology, arterial input function, fitting algorithms etc., leading to inaccurate diagnosis. In this paper, we have developed a novel Statistical Learning Algorithm for Tumor Segmentation (SLATS) based on Hidden Markov Models to auto-segment regions of angiogenesis, corresponding to tumor. The SLATS algorithm has been trained to identify voxels belonging to the tumor class using the time-intensity curve, first and second derivatives of the intensity curves (“velocity” and “acceleration” respectively) and a composite vector consisting of a concatenation of the intensity, velocity and acceleration vectors. The results of SLATS trained for the four vectors has been shown for 22 Invasive Ductal Carcinoma (IDC) and 19 Ductal Carcinoma In Situ (DCIS) cases. The SLATS trained for the velocity tuple shows the best performance in delineating the tumors when compared with the segmentation performed by an expert radiologist and the output of a commercially available software, CADstream. PMID:23693000

  2. Lars Onsager Prize: Optimization and learning algorithms from the theory of disordered systems

    NASA Astrophysics Data System (ADS)

    Zecchina, Riccardo

    The extraction of information from large amounts of data is one of the prominent cross disciplinary challenges in contemporary science. Solving inverse and learning problems over large scale data sets requires the design of efficient optimization algorithms over very large scale networks of constraints. In such a setting, critical phenomena of the type studied in statistical physics of disordered systems often play a crucial role. This observation has lead in the last decade to a cross fertilization between statistical physics, information theory and computer science, with applications in a variety of fields. In particular a deeper geometrical understanding of the ground state structure of random computational problems and novel classes of probabilistic algorithms have emerged. In this talk I will give a brief overview of these conceptual advances and I will discuss the role that subdominant states play in the design of algorithms for large scale optimization problems. I will conclude by showing how these ideas can lead to novel applications in computational neuroscience.

  3. Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine.

    PubMed

    Riccardi, Annalisa; Fernández-Navarro, Francisco; Carloni, Sante

    2014-10-01

    In this paper, the well known stagewise additive modeling using a multiclass exponential (SAMME) boosting algorithm is extended to address problems where there exists a natural order in the targets using a cost-sensitive approach. The proposed ensemble model uses an extreme learning machine (ELM) model as a base classifier (with the Gaussian kernel and the additional regularization parameter). The closed form of the derived weighted least squares problem is provided, and it is employed to estimate analytically the parameters connecting the hidden layer to the output layer at each iteration of the boosting algorithm. Compared to the state-of-the-art boosting algorithms, in particular those using ELM as base classifier, the suggested technique does not require the generation of a new training dataset at each iteration. The adoption of the weighted least squares formulation of the problem has been presented as an unbiased and alternative approach to the already existing ELM boosting techniques. Moreover, the addition of a cost model for weighting the patterns, according to the order of the targets, enables the classifier to tackle ordinal regression problems further. The proposed method has been validated by an experimental study by comparing it with already existing ensemble methods and ELM techniques for ordinal regression, showing competitive results.

  4. Pedagogical Distance: Explaining Misalignment in Student-Driven Online Learning Activities Using Activity Theory

    ERIC Educational Resources Information Center

    Westberry, Nicola; Franken, Margaret

    2015-01-01

    This paper provides an Activity Theory analysis of two online student-driven interactive learning activities to interrogate assumptions that such groups can effectively learn in the absence of the teacher. Such an analysis conceptualises learning tasks as constructed objects that drive pedagogical activity. The analysis shows a disconnect between…

  5. Application of Learning Machines and Combinatorial Algorithms in Water Resources Management and Hydrologic Sciences

    SciTech Connect

    Khalil, Abedalrazq F.; Kaheil, Yasir H.; Gill, Kashif; Mckee, Mac

    2010-01-01

    Contemporary and water resources engineering and management rely increasingly on pattern recognition techniques that have the ability to capitalize on the unrelenting accumulation of data that is made possible by modern information technology and remote sensing methods. In response to the growing information needs of modern water systems, advanced computational models and tools have been devised to identify and extract relevant information from the mass of data that is now available. This chapter presents innovative applications from computational learning science within the fields of hydrology, hydrogeology, hydroclimatology, and water management. The success of machine learning is evident from the growing number of studies involving the application of Artificial Neural Networks (ANN), Support Vector Machines (SVM), Relevance Vector Machines (RVM), and Locally Weighted Projection Regression (LWPR) to address various issues in hydrologic sciences. The applications that will be discussed within the chapter employ the abovementioned machine learning techniques for intelligent modeling of reservoir operations, temporal downscaling of precipitation, spatial downscaling of soil moisture and evapotranspiration, comparisons of various techniques for groundwater quality modeling, and forecasting of chaotic time series behavior. Combinatorial algorithms to capture the intrinsic complexities in the modeled phenomena and to overcome disparate scales are developed; for example, learning machines have been coupled with geostatistical techniques, non-homogenous hidden Markov models, wavelets, and evolutionary computing techniques. This chapter does not intend to be exhaustive; it reviews the progress that has been made over the past decade in the use of learning machines in applied hydrologic sciences and presents a summary of future needs and challenges for further advancement of these methods.

  6. An active noise control algorithm for controlling multiple sinusoids.

    PubMed

    Lee, S M; Lee, H J; Yoo, C H; Youn, D H; Cha, I W

    1998-07-01

    The filtered-x LMS algorithm and its modified versions have been successfully applied in suppressing acoustic noise such as single and multiple tones and broadband random noise. This paper presents an adaptive algorithm based on the filtered-x LMS algorithm which may be applied in attenuating tonal acoustic noise. In the proposed method, the weights of the adaptive filter and estimation of the phase shift due to the acoustic path from a loudspeaker to a microphone are computed simultaneously for optimal control. The algorithm possesses advantages over other filtered-x LMS approaches in three aspects: (1) each frequency component is processed separately using an adaptive filter with two coefficients, (2) the convergence parameter for each sinusoid can be selected independently, and (3) the computational load can be reduced by eliminating the convolution process required to obtain the filtered reference signal. Simulation results for a single-input/single-output (SISO) environment demonstrate that the proposed method is robust to the changes of the acoustic path between the actuator and the microphone and outperforms the filtered-x LMS algorithm in simplicity and convergence speed.

  7. Implementation of FFT Algorithm using DSP TMS320F28335 for Shunt Active Power Filter

    NASA Astrophysics Data System (ADS)

    Patel, Pinkal Jashvantbhai; Patel, Rajesh M.; Patel, Vinod

    2016-07-01

    This work presents simulation, analysis and experimental verification of Fast Fourier Transform (FFT) algorithm for shunt active power filter based on three-level inverter. Different types of filters can be used for elimination of harmonics in the power system. In this work, FFT algorithm for reference current generation is discussed. FFT control algorithm is verified using PSIM simulation results with DLL block and C-code. Simulation results are compared with experimental results for FFT algorithm using DSP TMS320F28335 for shunt active power filter application.

  8. A Framework for Adaptive E-Learning Based on Distributed Re-Usable Learning Activities.

    ERIC Educational Resources Information Center

    Brusilovsky, Peter; Nijhavan, Hemanta

    This paper suggests that a way to the new generation of powerful E-learning systems starts on the crossroads of two emerging fields: courseware re-use and adaptive educational systems. The paper presents the KnowledgeTree, a framework for adaptive E-learning based on distributed re-usable learning activities currently under development. The goal…

  9. Advancing the M-Learning Research Agenda for Active, Experiential Learning: Four Case Studies

    ERIC Educational Resources Information Center

    Dyson, Laurel Evelyn; Litchfield, Andrew; Lawrence, Elaine; Raban, Ryszard; Leijdekkers, Peter

    2009-01-01

    This article reports on an m-learning research agenda instituted at our university in order to explore how mobile technology can enhance active, experiential learning. Details of the implementation and results of four areas of m-learning are presented: mobile supported fieldwork, fostering interactivity in large lectures with mobile technology,…

  10. Experiential Learning and Learning Environments: The Case of Active Listening Skills

    ERIC Educational Resources Information Center

    Huerta-Wong, Juan Enrique; Schoech, Richard

    2010-01-01

    Social work education research frequently has suggested an interaction between teaching techniques and learning environments. However, this interaction has never been tested. This study compared virtual and face-to-face learning environments and included active listening concepts to test whether the effectiveness of learning environments depends…

  11. Multiliteracies and Active Learning in CLIL--The Development of Learn Web2.0

    ERIC Educational Resources Information Center

    Marenzi, I.; Zerr, S.

    2012-01-01

    This paper discusses the development of LearnWeb2.0, a search and collaboration environment for supporting searching, organizing, and sharing distributed resources, and our pedagogical setup based on the multiliteracies approach. In LearnWeb2.0, collaborative and active learning is supported through project-focused search and aggregation, with…

  12. Collegewide Promotion of E-Learning/Active Learning and Faculty Development

    ERIC Educational Resources Information Center

    Ogawa, Nobuyuki; Shimizu, Akira

    2016-01-01

    Japanese National Institutes of Technology have revealed a plan to strongly promote e-Learning and active learning under the common schematization of education in over 50 campuses nationwide. Our e-Learning and ICT-driven education practiced for more than fifteen years were highly evaluated, and is playing a leading role in promoting e-Learning…

  13. Students´ Perspectives on eLearning Activities in Person-Centered, Blended Learning Settings

    ERIC Educational Resources Information Center

    Haselberger, David; Motsching, Renate

    2016-01-01

    Blended or hybrid learning has become a frequent practice in higher education. In this article our primary research interest was to find out how students perceived eLearning activities in blended learning courses based on the person-centered paradigm. Through analyzing the content of a series of semi-structured interviews we found out that…

  14. Active learning of plans for safety and reachability goals with partial observability.

    PubMed

    Nam, Wonhong; Alur, Rajeev

    2010-04-01

    Traditional planning assumes reachability goals and/or full observability. In this paper, we propose a novel solution for safety and reachability planning with partial observability. Given a planning domain, a safety property, and a reachability goal, we automatically learn a safe permissive plan to guide the planning domain so that the safety property is not violated and that can force the planning domain to eventually reach states that satisfy the reachability goal, regardless of how the planning domain behaves. Our technique is based on the active learning of regular languages and symbolic model checking. The planning method first learns a safe plan using the L (*) algorithm, which is an efficient active learning algorithm for regular languages. We then check whether the safe plan learned is also permissive by Alternating-time Temporal Logic (ATL) model checking. If the plan is permissive, it is indeed a safe permissive plan. Otherwise, we identify and add a safe string to converge a safe permissive plan. We describe an implementation of the proposed technique and demonstrate that our tool can efficiently construct safe permissive plans for four sets of examples.

  15. Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

    PubMed

    Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

    2015-01-01

    Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

  16. A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

    NASA Astrophysics Data System (ADS)

    Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.

    2015-08-01

    Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

  17. Active-Learning Processes Used in US Pharmacy Education

    PubMed Central

    Brown, Stacy D.; Clavier, Cheri W.; Wyatt, Jarrett

    2011-01-01

    Objective To document the type and extent of active-learning techniques used in US colleges and schools of pharmacy as well as factors associated with use of these techniques. Methods A survey instrument was developed to assess whether and to what extent active learning was used by faculty members of US colleges and schools of pharmacy. This survey instrument was distributed via the American Association of Colleges of Pharmacy (AACP) mailing list. Results Ninety-five percent (114) of all US colleges and schools of pharmacy were represented with at least 1 survey among the 1179 responses received. Eighty-seven percent of respondents used active-learning techniques in their classroom activities. The heavier the teaching workload the more active-learning strategies were used. Other factors correlated with higher use of active-learning strategies included younger faculty member age (inverse relationship), lower faculty member rank (inverse relationship), and departments that focused on practice, clinical and social, behavioral, and/or administrative sciences. Conclusions Active learning has been embraced by pharmacy educators and is used to some extent by the majority of US colleges and schools of pharmacy. Future research should focus on how active-learning methods can be used most effectively within pharmacy education, how it can gain even broader acceptance throughout the academy, and how the effect of active learning on programmatic outcomes can be better documented. PMID:21769144

  18. Emergence of reproducible spatiotemporal activity during motor learning.

    PubMed

    Peters, Andrew J; Chen, Simon X; Komiyama, Takaki

    2014-06-12

    The motor cortex is capable of reliably driving complex movements yet exhibits considerable plasticity during motor learning. These observations suggest that the fundamental relationship between motor cortex activity and movement may not be fixed but is instead shaped by learning; however, to what extent and how motor learning shapes this relationship are not fully understood. Here we addressed this issue by using in vivo two-photon calcium imaging to monitor the activity of the same population of hundreds of layer 2/3 neurons while mice learned a forelimb lever-press task over two weeks. Excitatory and inhibitory neurons were identified by transgenic labelling. Inhibitory neuron activity was relatively stable and balanced local excitatory neuron activity on a movement-by-movement basis, whereas excitatory neuron activity showed higher dynamism during the initial phase of learning. The dynamics of excitatory neurons during the initial phase involved the expansion of the movement-related population which explored various activity patterns even during similar movements. This was followed by a refinement into a smaller population exhibiting reproducible spatiotemporal sequences of activity. This pattern of activity associated with the learned movement was unique to expert animals and not observed during similar movements made during the naive phase, and the relationship between neuronal activity and individual movements became more consistent with learning. These changes in population activity coincided with a transient increase in dendritic spine turnover in these neurons. Our results indicate that a novel and reproducible activity-movement relationship develops as a result of motor learning, and we speculate that synaptic plasticity within the motor cortex underlies the emergence of reproducible spatiotemporal activity patterns for learned movements. These results underscore the profound influence of learning on the way that the cortex produces movements.

  19. Structural Engineering. Technology Learning Activity. Teacher Edition. Technology Education Series.

    ERIC Educational Resources Information Center

    Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    This curriculum guide provides technology learning activities designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of…

  20. Active and Reflective Learning to Engage All Students

    ERIC Educational Resources Information Center

    McCoy, Bryan

    2013-01-01

    This article describes how teachers effectively manage learning through active engagement of all students throughout each class period. A case study is presented which demonstrates how students learn through active and reflective engagement with ideas, the environment, and other learners (National Middle School Association, 2010). The case study…

  1. Supporting "Learning by Design" Activities Using Group Blogs

    ERIC Educational Resources Information Center

    Fessakis, Georgios; Tatsis, Konstantinos; Dimitracopoulou, Angelique

    2008-01-01

    The paper presents a case study of the educational exploitation of group blogging for the implementation of a "learning by design" activity. More specifically, a group of students used a blog as a communication and information management tool in the University course of ICT-enhanced Geometry learning activities. The analysis of the designed…

  2. The Role of Active Learning in College Student Persistence

    ERIC Educational Resources Information Center

    Braxton, John M.; Jones, Willis A.; Hirschy, Amy S.; Hartley, Harold V., III

    2008-01-01

    Active learning, which entails any class activity that "involves students doing things and thinking about the things that they are doing," stands as an important pedagogical practice. Discussion, the types of questions faculty ask students in class, role playing, cooperative learning, debates, and the types of questions faculty ask on examinations…

  3. Incorporating Active Learning with Videos: A Case Study from Physics

    ERIC Educational Resources Information Center

    Lee, Kester J.; Sharma, Manjula D.

    2008-01-01

    Watching a video often results in passive learning and does not actively engage students. In this study, a class of 20 HSC Physics students were introduced to a teaching model that incorporated active learning principles with the watching of a video that explored the Meissner Effect and superconductors. Students would watch short sections of the…

  4. Brain Gym. Simple Activities for Whole Brain Learning.

    ERIC Educational Resources Information Center

    Dennison, Paul E.; Dennison, Gail E.

    This booklet contains simple movements and activities that are used with students in Educational Kinesiology to enhance their experience of whole brain learning. Whole brain learning through movement repatterning and Brain Gym activities enable students to access those parts of the brain previously unavailable to them. These movements of body and…

  5. Problem Solving. Technology Learning Activity. Teacher Edition. Technology Education Series.

    ERIC Educational Resources Information Center

    Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    This curriculum guide provides technology learning activities designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of…

  6. Tractor Mechanics: Learning Activity Packages 1-19.

    ERIC Educational Resources Information Center

    Clemson Univ., SC. Vocational Education Media Center.

    Learning activity packages are presented for teaching tractor mechanics. The first of two sections deals with miscellaneous tasks and contains learning activity packages on cleaning the tractor and receiving new tractor parts. Section 2 is concerned with maintaining and servicing the electrical system, and it includes the following learning…

  7. Teaching for Engagement: Part 3: Designing for Active Learning

    ERIC Educational Resources Information Center

    Hunter, William J.

    2015-01-01

    In the first two parts of this series, ("Teaching for Engagement: Part 1: Constructivist Principles, Case-Based Teaching, and Active Learning") and ("Teaching for Engagement: Part 2: Technology in the Service of Active Learning"), William J. Hunter sought to outline the theoretical rationale and research basis for such active…

  8. Teaching Sociological Theory through Active Learning: The Irrigation Exercise

    ERIC Educational Resources Information Center

    Holtzman, Mellisa

    2005-01-01

    For students, theory is often one of the most daunting aspects of sociology--it seems abstract, removed from the concrete events of their everyday lives, and therefore intimidating. In an attempt to break down student resistance to theory, instructors are increasingly turning to active learning approaches. Active learning exercises, then, appear…

  9. Two Learning Activities for a Large Introductory Statistics Class

    ERIC Educational Resources Information Center

    Zacharopoulou, Hrissoula

    2006-01-01

    In a very large Introductory Statistics class, i.e. in a class of more than 300 students, instructors may hesitate to apply active learning techniques, discouraged by the volume of extra work. In this paper two such activities are presented that evoke student involvement in the learning process. The first is group peer teaching and the second is…

  10. Students as Doers: Examples of Successful E-Learning Activities

    ERIC Educational Resources Information Center

    Tammelin, Maija; Peltonen, Berit; Puranen, Pasi; Auvinen, Lis

    2012-01-01

    This paper discusses learning language and communication activities that focus on students' concrete involvement in their learning process. The activities first deal with student-produced blogs and digital videos in business Spanish. They then present student-produced podcasts for Swedish business communication learners that are meant for speakers…

  11. Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

    PubMed

    Chang, Chin-Chun; Lin, Po-Yi

    2015-03-01

    The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach.

  12. BLGAN: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information.

    PubMed

    Sim, Kwang Mong; Guo, Yuanyuan; Shi, Benyun

    2009-02-01

    Automated negotiation provides a means for resolving differences among interacting agents. For negotiation with complete information, this paper provides mathematical proofs to show that an agent's optimal strategy can be computed using its opponent's reserve price (RP) and deadline. The impetus of this work is using the synergy of Bayesian learning (BL) and genetic algorithm (GA) to determine an agent's optimal strategy in negotiation (N) with incomplete information. BLGAN adopts: 1) BL and a deadline-estimation process for estimating an opponent's RP and deadline and 2) GA for generating a proposal at each negotiation round. Learning the RP and deadline of an opponent enables the GA in BLGAN to reduce the size of its search space (SP) by adaptively focusing its search on a specific region in the space of all possible proposals. SP is dynamically defined as a region around an agent's proposal P at each negotiation round. P is generated using the agent's optimal strategy determined using its estimations of its opponent's RP and deadline. Hence, the GA in BLGAN is more likely to generate proposals that are closer to the proposal generated by the optimal strategy. Using GA to search around a proposal generated by its current strategy, an agent in BLGAN compensates for possible errors in estimating its opponent's RP and deadline. Empirical results show that agents adopting BLGAN reached agreements successfully, and achieved: 1) higher utilities and better combined negotiation outcomes (CNOs) than agents that only adopt GA to generate their proposals, 2) higher utilities than agents that adopt BL to learn only RP, and 3) higher utilities and better CNOs than agents that do not learn their opponents' RPs and deadlines.

  13. Using active learning in lecture: best of "both worlds".

    PubMed

    Oermann, Marilyn H

    2004-01-01

    Many creative teaching strategies have been developed in recent years in nursing and other fields to promote active learning. These strategies foster development of problem solving, critical thinking, and communication skills, and they encourage students to work collaboratively with peers. However, in nurse educators' rush to embrace active learning, lecture has been viewed negatively by some faculty. Rather than positioning active learning against lecture, another approach is to integrate active learning within lecture, gaining the benefits of both methods. An integrated approach also takes into consideration the situation of teaching large groups of students. This article examines benefits of an integrated approach to teaching and presents strategies for active learning intended for use with lecture.

  14. A Wolf Pack Algorithm for Active and Reactive Power Coordinated Optimization in Active Distribution Network

    NASA Astrophysics Data System (ADS)

    Zhuang, H. M.; Jiang, X. J.

    2016-08-01

    This paper presents an active and reactive power dynamic optimization model for active distribution network (ADN), whose control variables include the output of distributed generations (DGs), charge or discharge power of energy storage system (ESS) and reactive power from capacitor banks. To solve the high-dimension nonlinear optimization model, a new heuristic swarm intelligent method, namely wolf pack algorithm (WPA) with better global convergence and computational robustness, is adapted so that the network loss minimization can be achieved. In this paper, the IEEE33-bus system is used to show the effectiveness of WPA technique compared with other techniques. Numerical tests on the modified IEEE 33-bus system show that WPA for active and reactive multi-period optimization of ADN is exact and effective.

  15. Neural activation during successful and unsuccessful verbal learning in schizophrenia.

    PubMed

    Heinze, Sibylle; Sartory, Gudrun; Müller, Bernhard W; de Greiff, Armin; Forsting, Michael; Jüptner, Markus

    2006-04-01

    Successful and unsuccessful intention to learn words was assessed by means of event-related functional MRI. Eighteen patients with schizophrenia and 15 healthy control participants were scanned while being given two word lists to read and another seven to learn with immediate recall. Neural activation patterns were segregated according to whether words were subsequently recalled or forgotten and these conditions were contrasted with each other and reading. Compared to controls, patients with schizophrenia showed deficits with regard to neural recruitment of right hippocampus and of cerebellar structures during successful verbal learning. Furthermore, a reversal of activated structures was evident in the two groups: Controls showed activation of right frontal and left middle temporal structures during the unsuccessful intention to learn. During successful learning, there was additional activation of right superior parietal lobule. In contrast, patients showed activation of right superior parietal lobule during unsuccessful and successful intention to learn. There were additional frontal and left middle temporal lobe activations during successful learning. We conclude that increased parietal activity may reflect a mechanism which compensates for the lack of hippocampal and cerebellar contributions to verbal learning in schizophrenia.

  16. Investigating methods of improving SSM/I and OKEAN sea ice inversion parameters using MLP neural networks with different learning algorithms.

    NASA Astrophysics Data System (ADS)

    Belchansky, G.; Alpatsky, I.; Mordvintsev, I.; Douglas, D.

    Investigating new methods to estimate sea-ice geophysical parameters using multisensor satellite data is critical for global change studies. The most widely used and consistent data to study sea ice at global scale are SMMR and SSM/I passive microwave measurements available since 1978. However, comparisons with LANDSAT, AVHRR and ERS-1 SAR have demonstrated substantial seasonal and regional differences in SSM/I ice parameter estimates (Belchansky and Douglas, 2000, 2002). This report presents investigating methods of improving SSM/I and OKEAN sea ice inversion parameters using MLP neural networks, and compare the sea ice classification results from different neural networks and linear mixture model. Efficiency of four sea ice type inversion (classification) algorithms utilizing SSM/I, OKEAN-01, ERS and RADARSAT satellite data were compared and investigated. The first one applied different linear mixture models (NASA Team, Bootstrap, and OKEAN). The second, third and fourth algorithms applied the modified MLP neural networks with different learning algorithms based, respectively, on 1) error back propagation and simulated annealing (Kirkpatrick, 1983); 2) dynamic learning and polynomial basis function (Chen et al., 1996); and 3) dynamic learning and two-step optimization. Both last algorithms used the Kalman filtering technique. Our studies demonstrated that both modified MLP neural networks with dynamic learning were more efficient (in terms of learning time, accuracy, and ability to generalize the selected learning data) than modified MLP neural network with learning algorithms based on the error back propagation and simulated annealing for simple approximation problems. MY sea ice and albedo inversion from SSM/I brightness temperatures and respective OKEAN learning data sets demonstrated that these algorithms caused over-fitting in comparison with the MLP neural network with the error back propagation and simulated annealing. Therefore, for MY sea ice inversion

  17. From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms.

    PubMed

    Shao, Ling; Yan, Ruomei; Li, Xuelong; Liu, Yan

    2014-07-01

    Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.

  18. [An improved N-FINDR endmember extraction algorithm based on manifold learning and spatial information].

    PubMed

    Tang, Xiao-yan; Gao, Kun; Ni, Guo-qiang; Zhu, Zhen-yu; Cheng, Hao-bo

    2013-09-01

    An improved N-FINDR endmember extraction algorithm by combining manifold learning and spatial information is presented under nonlinear mixing assumptions. Firstly, adaptive local tangent space alignment is adapted to seek potential intrinsic low-dimensional structures of hyperspectral high-diemensional data and reduce original data into a low-dimensional space. Secondly, spatial preprocessing is used by enhancing each pixel vector in spatially homogeneous areas, according to the continuity of spatial distribution of the materials. Finally, endmembers are extracted by looking for the largest simplex volume. The proposed method can increase the precision of endmember extraction by solving the nonlinearity of hyperspectral data and taking advantage of spatial information. Experimental results on simulated and real hyperspectral data demonstrate that the proposed approach outperformed the geodesic simplex volume maximization (GSVM), vertex component analysis (VCA) and spatial preprocessing N-FINDR method (SPPNFINDR).

  19. Orchestrating Learning Activities Using the CADMOS Learning Design Tool

    ERIC Educational Resources Information Center

    Katsamani, Maria; Retalis, Symeon

    2013-01-01

    This paper gives an overview of CADMOS (CoursewAre Development Methodology for Open instructional Systems), a graphical IMS-LD Level A & B compliant learning design (LD) tool, which promotes the concept of "separation of concerns" during the design process, via the creation of two models: the conceptual model, which describes the…

  20. Learning To Learn: 15 Vocabulary Acquisition Activities. Tips and Hints.

    ERIC Educational Resources Information Center

    Holden, William R.

    1999-01-01

    This article describes a variety of ways learners can help themselves remember new words, choosing the ones that best suit their learning styles. It is asserted that repeated exposure to new lexical items using a variety of means is the most consistent predictor of retention. The use of verbal, visual, tactile, textual, kinesthetic, and sonic…

  1. Learning Design--Creative Design to Visualise Learning Activities

    ERIC Educational Resources Information Center

    Toetenel, Lisette; Rienties, Bart

    2016-01-01

    The focus on quality improvements by institutions for better online and blended teaching can be delivered in different ways. This article reports on the implementation of this process and the approaches taken first, in terms of the design of new learning materials, and second, when reviewing the existing curriculum. The study aims to ascertain…

  2. Analysis and mapping of mountain permafrost data: a comparison between two machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

    2015-04-01

    Within the Alps, knowledge on mountain permafrost characteristics (thermal state and related processes) and distribution has significantly increased within the last 15 years thanks to many field studies and monitoring projects. They reveal the complexity of mountain permafrost, both in term of spatial repartition and sensitivity to current warming. This can be illustrated by the situation in talus slopes, where permafrost is only present in the lower part of the landform in reason of a reversible mechanism of air advection that leads to negative thermal anomalies downslope and to positive ones upslope. Moreover, performances of existing equilibrium models are optimal basically only at a local or regional scale. At the micro scale, the nonlinear interrelationship that exists between the climatic components and the terrain surface/subsurface properties controlling the occurrence of permafrost is not well considered by this type of models. We often need to appeal to physical models, which are sometimes difficult to calibrate, often require heavy computational power to run and become increasingly complex as the amounts of empirical data grow. By disposing of a large amount of spatial data, the application of robust and nonlinear algorithms is possible. In the present study, we investigate the potential of two classification algorithms that belong to machine learning (ML) domain: Random Forest (RF) and Support Vector Machines (SVM). With ML algorithms, functional dependencies are derived directly from data (a dataset of thousands of field observations and topo-climatic data). With this approach data speak for themselves and there is any need to appeal to physical models. The RF algorithm has recently gained a great popularity and also provides a weight of the contribution of each variable. This measure can be used to detect and display the main factors affecting the studied phenomenon. The SVM has proven to be efficient in past permafrost distribution modelling attempts

  3. Creating Stimulating Learning and Thinking Using New Models of Activity-Based Learning and Metacognitive-Based Activities

    ERIC Educational Resources Information Center

    Pang, Katherine

    2010-01-01

    The purpose of this paper is to present a novel way to stimulate learning, creativity, and thinking based on a new understanding of activity-based learning (ABL) and two methods for developing metacognitive-based activities for the classroom. ABL, in this model, is based on the premise that teachers are distillers and facilitators of information…

  4. Learning Microbiology through Cooperation: Designing Cooperative Learning Activities That Promote Interdependence, Interaction, and Accountability.

    ERIC Educational Resources Information Center

    Trempy, Janine E.; Skinner, Monica M.; Siebold, William A.

    2002-01-01

    Describes the course "The World According to Microbes" which puts science, mathematics, engineering, and technology majors into teams of students charged with problem solving activities that are microbial in origin. Describes the development of learning activities that utilize key components of cooperative learning including positive…

  5. Human activity recognition based on feature selection in smart home using back-propagation algorithm.

    PubMed

    Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

    2014-09-01

    In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM.

  6. Synthetic tests of passive microwave brightness temperature assimilation over snow covered land using machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Forman, B. A.

    2015-12-01

    A novel data assimilation framework is evaluated that assimilates passive microwave (PMW) brightness temperature (Tb) observations into an advanced land surface model for the purpose of improving snow depth and snow water equivalent (SWE) estimates across regional- and continental-scales. The multifrequency, multipolarization framework employs machine learning algorithms to predict PMW Tb as a function of land surface model state information and subsequently merges the predicted PMW Tb with observed PMW Tb from the Advanced Microwave Scanning Radiometer (AMSR-E). The merging procedure is predicated on conditional probabilities computed within a Bayesian statistical framework using either an Ensemble Kalman Filter (EnKF) or an Ensemble Kalman Smoother (EnKS). The data assimilation routine produces a conditioned (updated) estimate of modeled SWE that is more accurate and contains less uncertainty than the model without assimilation. A synthetic case study is presented for select locations in North America that compares model results with and without assimilation against synthetic observations of snow depth and SWE. It is shown that the data assimilation framework improves modeled estimates of snow depth and SWE during both the accumulation and ablation phases of the snow season. Further, it is demonstrated that the EnKS outperforms the EnKF implementation due to its ability to better modulate high frequency noise into the conditioned estimates. The overarching findings from this study demonstrate the feasibility of machine learning algorithms for use as an observation model operator within a data assimilation framework in order to improve model estimates of snow depth and SWE across regional- and continental-scales.

  7. Learning Choices, Older Australians and Active Ageing

    ERIC Educational Resources Information Center

    Boulton-Lewis, Gillian M.; Buys, Laurie

    2015-01-01

    This paper reports on the findings of qualitative, semistructured interviews conducted with 40 older Australian participants who either did or did not engage in organized learning. Phenomenology was used to guide the interviews and analysis to explore the lived learning experiences and perspectives of these older people. Their experiences of…

  8. Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

    PubMed

    Singh, Swadha; Singh, Raghvendra

    2017-03-01

    Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of genome and transcriptome sequence data and comparative genomics provide unprecedented opportunities to identify riboswitches in the genome. In the present study, we have evaluated the following six machine learning algorithms for their efficiency to classify riboswitches: J48, BayesNet, Naïve Bayes, Multilayer Perceptron, sequential minimal optimization, hidden Markov model (HMM). For determining effective classifier, the algorithms were compared on the statistical measures of specificity, sensitivity, accuracy, F-measure and receiver operating characteristic (ROC) plot analysis. The classifier Multilayer Perceptron achieved the best performance, with the highest specificity, sensitivity, F-score and accuracy, and with the largest area under the ROC curve, whereas HMM was the poorest performer. At present, the available tools for the prediction and classification of riboswitches are based on covariance model, support vector machine and HMM. The present study determines Multilayer Perceptron as a better classifier for the genome-wide riboswitch searches.

  9. Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

    NASA Technical Reports Server (NTRS)

    Das, Santanu; Srivastava, Ashok N.; Matthews, Bryan L.; Oza, Nikunj C.

    2010-01-01

    The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

  10. The use of machine learning algorithms to design a generalized simplified denitrification model

    NASA Astrophysics Data System (ADS)

    Oehler, F.; Rutherford, J. C.; Coco, G.

    2010-04-01

    We designed generalized simplified models using machine learning algorithms (ML) to assess denitrification at the catchment scale. In particular, we designed an artificial neural network (ANN) to simulate total nitrogen emissions from the denitrification process. Boosted regression trees (BRT, another ML) was also used to analyse the relationships and the relative influences of different input variables towards total denitrification. To calibrate the ANN and BRT models, we used a large database obtained by collating datasets from the literature. We developed a simple methodology to give confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS. NEMIS is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generality of the ANN model may also be improved if pH is used to differentiate between soil types. Further improvements in model performance can be achieved by lessening dataset effects.

  11. The use of machine learning algorithms to design a generalized simplified denitrification model

    NASA Astrophysics Data System (ADS)

    Oehler, F.; Rutherford, J. C.; Coco, G.

    2010-10-01

    We propose to use machine learning (ML) algorithms to design a simplified denitrification model. Boosted regression trees (BRT) and artificial neural networks (ANN) were used to analyse the relationships and the relative influences of different input variables towards total denitrification, and an ANN was designed as a simplified model to simulate total nitrogen emissions from the denitrification process. To calibrate the BRT and ANN models and test this method, we used a database obtained collating datasets from the literature. We used bootstrapping to compute confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS, which is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generalization, although limited to the data space of the database used to build the ML models, could be improved if pH is used to differentiate between soil types. Further improvements in model performance and generalization could be achieved by adding more data.

  12. MLS student active learning within a "cloud" technology program.

    PubMed

    Tille, Patricia M; Hall, Heather

    2011-01-01

    In November 2009, the MLS program in a large public university serving a geographically large, sparsely populated state instituted an initiative for the integration of technology enhanced teaching and learning within the curriculum. This paper is intended to provide an introduction to the system requirements and sample instructional exercises used to create an active learning technology-based classroom. Discussion includes the following: 1.) define active learning and the essential components, 2.) summarize teaching methods, technology and exercises utilized within a "cloud" technology program, 3.) describe a "cloud" enhanced classroom and programming 4.) identify active learning tools and exercises that can be implemented into laboratory science programs, and 5.) describe the evaluation and assessment of curriculum changes and student outcomes. The integration of technology in the MLS program is a continual process and is intended to provide student-driven active learning experiences.

  13. Feasibility of Active Machine Learning for Multiclass Compound Classification.

    PubMed

    Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

    2016-01-25

    A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

  14. Predicting reading and mathematics from neural activity for feedback learning.

    PubMed

    Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A

    2017-01-01

    Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task predicted reading and mathematics performance 2 years later. The results indicated that feedback learning performance predicted both reading and mathematics performance. Activity during feedback learning in left superior dorsolateral prefrontal cortex (DLPFC) predicted reading performance, whereas activity in presupplementary motor area/anterior cingulate cortex (pre-SMA/ACC) predicted mathematical performance. Moreover, left superior DLPFC and pre-SMA/ACC activity predicted unique variance in reading and mathematics ability over behavioral testing of feedback learning performance alone. These results provide valuable insights into the relationship between laboratory-based learning tasks and learning in school settings, and the value of neural assessments for prediction of school performance over behavioral testing alone. (PsycINFO Database Record

  15. Psychological and Pedagogic Conditions of Activating Creative Activity in Students for Successful Learning

    ERIC Educational Resources Information Center

    Abykanova, Bakytgul; Bilyalova, Zhupar; Makhatova, Valentina; Idrissov, Salamat; Nugumanov, Samal

    2016-01-01

    Creative activity of a pedagogic process subject depends on the pedagogue's position, on his faith in the abilities to learn successfully, on encouragement of achievements, stimulating the initiative and activity. Successful learning by activating creative activity is possible with the presence of respectful attitude towards the pedagogic process…

  16. Extremely Randomized Machine Learning Methods for Compound Activity Prediction.

    PubMed

    Czarnecki, Wojciech M; Podlewska, Sabina; Bojarski, Andrzej J

    2015-11-09

    Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called 'extremely randomized methods'-Extreme Entropy Machine and Extremely Randomized Trees-for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their 'non-extreme' competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  17. Machine learning assisted design of highly active peptides for drug discovery.

    PubMed

    Giguère, Sébastien; Laviolette, François; Marchand, Mario; Tremblay, Denise; Moineau, Sylvain; Liang, Xinxia; Biron, Éric; Corbeil, Jacques

    2015-04-01

    The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at http://graal.ift.ulaval.ca/peptide-design/.

  18. Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery

    PubMed Central

    Giguère, Sébastien; Laviolette, François; Marchand, Mario; Tremblay, Denise; Moineau, Sylvain; Liang, Xinxia; Biron, Éric; Corbeil, Jacques

    2015-01-01

    The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at http://graal.ift.ulaval.ca/peptide-design/. PMID:25849257

  19. Field tests and machine learning approaches for refining algorithms and correlations of driver's model parameters.

    PubMed

    Tango, Fabio; Minin, Luca; Tesauri, Francesco; Montanari, Roberto

    2010-03-01

    This paper describes the field tests on a driving simulator carried out to validate the algorithms and the correlations of dynamic parameters, specifically driving task demand and drivers' distraction, able to predict drivers' intentions. These parameters belong to the driver's model developed by AIDE (Adaptive Integrated Driver-vehicle InterfacE) European Integrated Project. Drivers' behavioural data have been collected from the simulator tests to model and validate these parameters using machine learning techniques, specifically the adaptive neuro fuzzy inference systems (ANFIS) and the artificial neural network (ANN). Two models of task demand and distraction have been developed, one for each adopted technique. The paper provides an overview of the driver's model, the description of the task demand and distraction modelling and the tests conducted for the validation of these parameters. A test comparing predicted and expected outcomes of the modelled parameters for each machine learning technique has been carried out: for distraction, in particular, promising results (low prediction errors) have been obtained by adopting an artificial neural network.

  20. An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

    PubMed

    Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

    2015-08-13

    It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

  1. An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

    PubMed Central

    Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

    2015-01-01

    It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

  2. Learning Syntactic Rules and Tags with Genetic Algorithms for Information Retrieval and Filtering: An Empirical Basis for Grammatical Rules.

    ERIC Educational Resources Information Center

    Losee, Robert M.

    1996-01-01

    The grammars of natural languages may be learned by using genetic algorithm systems such as LUST (Linguistics Using Sexual Techniques) that reproduce and mutate grammatical rules and parts-of-speech tags. In document retrieval or filtering systems, applying tags to the list of terms representing a document provides additional information about…

  3. Simulation of rat behavior by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals.

    PubMed

    Murakoshi, Kazushi; Noguchi, Takuya

    2005-04-01

    Brown and Wanger [Brown, R.T., Wanger, A.R., 1964. Resistance to punishment and extinction following training with shock or nonreinforcement. J. Exp. Psychol. 68, 503-507] investigated rat behaviors with the following features: (1) rats were exposed to reward and punishment at the same time, (2) environment changed and rats relearned, and (3) rats were stochastically exposed to reward and punishment. The results are that exposure to nonreinforcement produces resistance to the decremental effects of behavior after stochastic reward schedule and that exposure to both punishment and reinforcement produces resistance to the decremental effects of behavior after stochastic punishment schedule. This paper aims to simulate the rat behaviors by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals. The former algorithms of reinforcement learning were unable to simulate the behavior of the feature (3). We improve the former reinforcement learning algorithms by controlling learning parameters in consideration of the acquisition probabilities of reinforcement signals. The proposed algorithm qualitatively simulates the result of the animal experiment of Brown and Wanger.

  4. Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion.

    PubMed

    Du, Bo; Wang, Zengmao; Zhang, Lefei; Zhang, Liangpei; Tao, Dacheng

    2017-04-01

    Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs. In multi-label active learning, some researches have been done for querying the relevant labels with less training samples or querying all labels without diagnosing the discriminative information. They all cannot effectively handle the outlier labels for the measurement of uncertainty. Since maximum correntropy criterion (MCC) provides a robust analysis for outliers in many machine learning and data mining algorithms, in this paper, we derive a robust multi-label active learning algorithm based on an MCC by merging uncertainty and representativeness, and propose an efficient alternating optimization method to solve it. With MCC, our method can eliminate the influence of outlier labels that are not discriminative to measure the uncertainty. To make further improvement on the ability of information measurement, we merge uncertainty and representativeness with the prediction labels of unknown data. It cannot only enhance the uncertainty but also improve the similarity measurement of multi-label data with labels information. Experiments on benchmark multi-label data sets have shown a superior performance than the state-of-the-art methods.

  5. Human dorsal striatal activity during choice discriminates reinforcement learning behavior from the gambler's fallacy.

    PubMed

    Jessup, Ryan K; O'Doherty, John P

    2011-04-27

    Reinforcement learning theory has generated substantial interest in neurobiology, particularly because of the resemblance between phasic dopamine and reward prediction errors. Actor-critic theories have been adapted to account for the functions of the striatum, with parts of the dorsal striatum equated to the actor. Here, we specifically test whether the human dorsal striatum--as predicted by an actor-critic instantiation--is used on a trial-to-trial basis at the time of choice to choose in accordance with reinforcement learning theory, as opposed to a competing strategy: the gambler's fallacy. Using a partial-brain functional magnetic resonance imaging scanning protocol focused on the striatum and other ventral brain areas, we found that the dorsal striatum is more active when choosing consistent with reinforcement learning compared with the competing strategy. Moreover, an overlapping area of dorsal striatum along with the ventral striatum was found to be correlated with reward prediction errors at the time of outcome, as predicted by the actor-critic framework. These findings suggest that the same region of dorsal striatum involved in learning stimulus-response associations may contribute to the control of behavior during choice, thereby using those learned associations. Intriguingly, neither reinforcement learning nor the gambler's fallacy conformed to the optimal choice strategy on the specific decision-making task we used. Thus, the dorsal striatum may contribute to the control of behavior according to reinforcement learning even when the prescriptions of such an algorithm are suboptimal in terms of maximizing future rewards.

  6. An algorithm for finding biologically significant features in microarray data based on a priori manifold learning.

    PubMed

    Hira, Zena M; Trigeorgis, George; Gillies, Duncan F

    2014-01-01

    Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA) which assumes a multivariate Gaussian model of the data. More recently, non-linear methods have been investigated. Among these, manifold learning algorithms, for example Isomap, aim to project the data from a higher dimensional space onto a lower dimension one. We have proposed a priori manifold learning for finding a manifold in which a representative set of microarray data is fused with relevant data taken from the KEGG pathway database. Once the manifold has been constructed the raw microarray data is projected onto it and clustering and classification can take place. In contrast to earlier fusion based methods, the prior knowledge from the KEGG databases is not used in, and does not bias the classification process--it merely acts as an aid to find the best space in which to search the data. In our experiments we have found that using our new manifold method gives better classification results than using either PCA or conventional Isomap.

  7. Storage capacity and learning algorithms for two-layer neural networks

    NASA Astrophysics Data System (ADS)

    Engel, A.; Köhler, H. M.; Tschepke, F.; Vollmayr, H.; Zippelius, A.

    1992-05-01

    A two-layer feedforward network of McCulloch-Pitts neurons with N inputs and K hidden units is analyzed for N-->∞ and K finite with respect to its ability to implement p=αN random input-output relations. Special emphasis is put on the case where all hidden units are coupled to the output with the same strength (committee machine) and the receptive fields of the hidden units either enclose all input units (fully connected) or are nonoverlapping (tree structure). The storage capacity is determined generalizing Gardner's treatment [J. Phys. A 21, 257 (1988); Europhys. Lett. 4, 481 (1987)] of the single-layer perceptron. For the treelike architecture, a replica-symmetric calculation yields αc~ √K for a large number K of hidden units. This result violates an upper bound derived by Mitchison and Durbin [Biol. Cybern. 60, 345 (1989)]. One-step replica-symmetry breaking gives lower values of αc. In the fully connected committee machine there are in general correlations among different hidden units. As the limit of capacity is approached, the hidden units are anticorrelated: One hidden unit attempts to learn those patterns which have not been learned by the others. These correlations decrease as 1/K, so that for K-->∞ the capacity per synapse is the same as for the tree architecture, whereas for small K we find a considerable enhancement for the storage per synapse. Numerical simulations were performed to explicitly construct solutions for the tree as well as the fully connected architecture. A learning algorithm is suggested. It is based on the least-action algorithm, which is modified to take advantage of the two-layer structure. The numerical simulations yield capacities p that are slightly more than twice the number of degrees of freedom, while the fully connected net can store relatively more patterns than the tree. Various generalizations are discussed. Variable weights from hidden to output give the same results for the storage capacity as does the committee

  8. SAN-RL: combining spreading activation networks and reinforcement learning to learn configurable behaviors

    NASA Astrophysics Data System (ADS)

    Gaines, Daniel M.; Wilkes, Don M.; Kusumalnukool, Kanok; Thongchai, Siripun; Kawamura, Kazuhiko; White, John H.

    2002-02-01

    Reinforcement learning techniques have been successful in allowing an agent to learn a policy for achieving tasks. The overall behavior of the agent can be controlled with an appropriate reward function. However, the policy that is learned will be fixed to this reward function. If the user wishes to change his or her preference about how the task is achieved the agent must be retrained with this new reward function. We address this challenge by combining Spreading Activation Networks and Reinforcement Learning in an approach we call SAN-RL. This approach provides the agent with a causal structure, the spreading activation network, relating goals to the actions that can achieve those goals. This enables the agent to select actions relative to the goal priorities. We combine this with reinforcement learning to enable the agent to learn a policy. Together, these approaches enable the learning of a configurable behaviors, a policy that can be adapted to meet the current preferences. We compare the approach with Q-learning on a robot navigation task. We demonstrate that SAN-RL exhibits goal-directed behavior before learning, exploits the causal structure of the network to focus its search during learning and results in configurable behaviors after learning.

  9. The Shoe Box Curriculum: Practical Ideas for Active Learning.

    ERIC Educational Resources Information Center

    Molnar, Alex; Roy, Will

    This book contains 65 specific activities designed to help disadvantaged students learn to use language more skillfully and develop the ability to function well in the school environment. The descriptions of the activities are referred to as shoe box labs and generally include the title of the activity, instructions for performing the activity,…

  10. Prediction of Anti-inflammatory Plants and Discovery of Their Biomarkers by Machine Learning Algorithms and Metabolomic Studies.

    PubMed

    Chagas-Paula, Daniela Aparecida; Oliveira, Tiago Branquinho; Zhang, Tong; Edrada-Ebel, RuAngelie; Da Costa, Fernando Batista

    2015-04-01

    Nonsteroidal anti-inflammatory drugs are the most used anti-inflammatory medicines in the world. Side effects still occur, however, and some inflammatory pathologies lack efficient treatment. Cyclooxygenase and lipoxygenase pathways are of utmost importance in inflammatory processes; therefore, novel inhibitors are currently needed for both of them. Dual inhibitors of cyclooxygenase-1 and 5-lipoxygenase are anti-inflammatory drugs with high efficacy and low side effects. In this work, 57 leaf extracts (EtOH-H2O 7 : 3, v/v) from Asteraceae species with in vitro dual inhibition of cyclooxygenase-1 and 5-lipoxygenase were analyzed by high-performance liquid chromatography-high-resolution-ORBITRAP-mass spectrometry analysis and subjected to in silico studies using machine learning algorithms. The data from all samples were processed by employing differential expression analysis software coupled to the Dictionary of Natural Products for dereplication studies. The 6052 chromatographic peaks (ESI positive and negative modes) of the extracts were selected by a genetic algorithm according to their respective anti-inflammatory properties; after this procedure, 1241 of them remained. A study using a decision tree classifier was carried out, and 11 compounds were determined to be biomarkers due to their anti-inflammatory potential. Finally, a model to predict new biologically active extracts from Asteraceae species using liquid chromatography-mass spectrometry information with no prior knowledge of their biological data was built using a multilayer perceptron (artificial neural networks) with the back-propagation algorithm using the biomarker data. As a result, a new and robust artificial neural network model for predicting the anti-inflammatory activity of natural compounds was obtained, resulting in a high percentage of correct predictions (81 %), high precision (100 %) for dual inhibition, and low error values (mean absolute error = 0.3), as also shown in the

  11. Moments of movement: active learning and practice development.

    PubMed

    Dewing, Jan

    2010-01-01

    As our understanding of practice development becomes more sophisticated, we enhance our understanding of how the facilitation of learning in and from practice, can be more effectively achieved. This paper outlines an approach for enabling and maximizing learning within practice development known as 'Active Learning'. It considers how, given establishing a learning culture is a prerequisite for the sustainability of PD within organisations, practice developers can do more to maximize learning for practitioners and other stakeholders. Active Learning requires that more attention be given by organisations committed to PD, at a corporate and strategic level for how learning strategies are developed in the workplace. Specifically, a move away from a heavy reliance on training may be required. Practice development facilitators also need to review: how they organise and offer learning, so that learning strategies are consistent with the vision, aims and processes of PD; have skills in the planning, delivery and evaluation of learning as part of their role and influence others who provide more traditional methods of training and education.

  12. Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

    NASA Astrophysics Data System (ADS)

    Biswas, Rahul; Blackburn, Lindy; Cao, Junwei; Essick, Reed; Hodge, Kari Alison; Katsavounidis, Erotokritos; Kim, Kyungmin; Kim, Young-Min; Le Bigot, Eric-Olivier; Lee, Chang-Hwan; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.; Tao, Ye; Vaulin, Ruslan; Wang, Xiaoge

    2013-09-01

    The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple detectors is non-negligible. These “glitches” can easily be mistaken for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests. These classifiers identify and remove a substantial fraction of the glitches present in two different data sets: four weeks of LIGO’s fourth science run and one week of LIGO’s sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth-science-run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar performance to the benchmark algorithm, the ordered veto list, which is optimized to detect pairwise correlations between transients in LIGO auxiliary channels and glitches in the gravitational-wave data. This suggests that most of the useful information currently extracted from the auxiliary channels is already described

  13. Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

    NASA Technical Reports Server (NTRS)

    Jayroe, R. R., Jr.

    1977-01-01

    An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described.

  14. Using assistive technology adaptations to include students with learning disabilities in cooperative learning activities.

    PubMed

    Bryant, D P; Bryant, B R

    1998-01-01

    Cooperative learning (CL) is a common instructional arrangement that is used by classroom teachers to foster academic achievement and social acceptance of students with and without learning disabilities. Cooperative learning is appealing to classroom teachers because it can provide an opportunity for more instruction and feedback by peers than can be provided by teachers to individual students who require extra assistance. Recent studies suggest that students with LD may need adaptations during cooperative learning activities. The use of assistive technology adaptations may be necessary to help some students with LD compensate for their specific learning difficulties so that they can engage more readily in cooperative learning activities. A process for integrating technology adaptations into cooperative learning activities is discussed in terms of three components: selecting adaptations, monitoring the use of the adaptations during cooperative learning activities, and evaluating the adaptations' effectiveness. The article concludes with comments regarding barriers to and support systems for technology integration, technology and effective instructional practices, and the need to consider technology adaptations for students who have learning disabilities.

  15. Interactive lecture demonstrations, active learning, and the ALOP project

    NASA Astrophysics Data System (ADS)

    Lakshminarayanan, Vasudevan

    2011-05-01

    There is considerable evidence from the physics education literature that traditional approaches are ineffective in teaching physics concepts. A better teaching method is to use the active learning environment, which can be created using interactive lecture demonstrations. Based on the active learning methodology and within the framework of the UNESCO mandate in physics education and introductory physics, the ALOP project (active learning in optics and photonics) was started in 2003, to provide a focus on an experimental area that is adaptable and relevant to research and educational conditions in many developing countries. This project is discussed in this paper.

  16. Active controllers and the time duration to learn a task

    NASA Technical Reports Server (NTRS)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  17. Vibration control of a nonlinear quarter-car active suspension system by reinforcement learning

    NASA Astrophysics Data System (ADS)

    Bucak, İ. Ö.; Öz, H. R.

    2012-06-01

    This article presents the investigation of performance of a nonlinear quarter-car active suspension system with a stochastic real-valued reinforcement learning control strategy. As an example, a model of a quarter car with a nonlinear suspension spring subjected to excitation from a road profile is considered. The excitation is realised by the roughness of the road. The quarter-car model to be considered here can be approximately described as a nonlinear two degrees of freedom system. The experimental results indicate that the proposed active suspension system suppresses the vibrations greatly. A simulation of a nonlinear quarter-car active suspension system is presented to demonstrate the effectiveness and examine the performance of the learning control algorithm.

  18. Active and Collaborative Learning in an Undergraduate Sociological Theory Course

    ERIC Educational Resources Information Center

    Pedersen, Daphne E.

    2010-01-01

    In this article, the author describes the use of active and collaborative learning strategies in an undergraduate sociological theory course. A semester-long ethnographic project is the foundation for the course; both individual and group participation contribute to the learning process. Assessment findings indicate that students are able, through…

  19. Model Activity Systems: Dialogic Teacher Learning for Social Justice Teaching

    ERIC Educational Resources Information Center

    Hoffman-Kipp, Peter

    2003-01-01

    Interest of teacher educators working in the field of social justice focuses on the ways in which teachers learn to inscribe their professional activity within social movements (for progressive change. The community of practice (COP) approach to understanding learning as a social process has a lot of currency right now in teacher education…

  20. Learner-Interface Interaction for Technology-Enhanced Active Learning

    ERIC Educational Resources Information Center

    Sinha, Neelu; Khreisat, Laila; Sharma, Kiron

    2009-01-01

    Neelu Sinha, Laila Khreisat, and Kiron Sharma describe how learner-interface interaction promotes active learning in computer science education. In a pilot study using technology that combines DyKnow software with a hardware platform of pen-enabled HP Tablet notebook computers, Sinha, Khreisat, and Sharma created dynamic learning environments by…

  1. Intergenerational Service Learning with Elders: Multidisciplinary Activities and Outcomes

    ERIC Educational Resources Information Center

    Krout, John A.; Bergman, Elizabeth; Bianconi, Penny; Caldwell, Kathryn; Dorsey, Julie; Durnford, Susan; Erickson, Mary Ann; Lapp, Julia; Monroe, Janice Elich; Pogorzala, Christine; Taves, Jessica Valdez

    2010-01-01

    This article provides an overview of the activities included in a 3-year, multidisciplinary, intergenerational service-learning project conducted as part of a Foundation for Long-Term Care Service Learning: Linking Three Generations grant. Courses from four departments (gerontology, psychology, occupational therapy, and health promotion and…

  2. Active Learning in a Math for Liberal Arts Classroom

    ERIC Educational Resources Information Center

    Lenz, Laurie

    2015-01-01

    Inquiry-based learning is a topic of growing interest in the mathematical community. Much of the focus has been on using these methods in calculus and higher-level classes. This article describes the design and implementation of a set of inquiry-based learning activities in a Math for Liberal Arts course at a small, private, Catholic college.…

  3. Who Benefits from Cooperative Learning with Movement Activity?

    ERIC Educational Resources Information Center

    Shoval, Ella; Shulruf, Boaz

    2011-01-01

    The goal of this study is to identify learners who are most likely to benefit from a small group cooperative learning strategy, which includes tasks involving movement activities. The study comprised 158 learners from five second and third grade classes learning about angles. The research tools included structured observation of each learner and…

  4. Critique in Academic Disciplines and Active Learning of Academic Content

    ERIC Educational Resources Information Center

    Ford, Michael J.

    2010-01-01

    This article argues for increased theoretical specificity in the active learning process. Whereas constructivist learning emphasizes construction of meaning, the process articulated here complements meaning construction with disciplinary critique. This process is an implication of how disciplinary communities generate new knowledge claims, which…

  5. Creating Activating Events for Transformative Learning in a Prison Classroom

    ERIC Educational Resources Information Center

    Keen, Cheryl H.; Woods, Robert

    2016-01-01

    In this article, we interpreted, in light of Mezirow's theory of transformative learning, interviews with 13 educators regarding their work with marginalized adult learners in prisons in the northeastern United States. Transformative learning may have been aided by the educators' response to unplanned activating events, humor, and respect, and…

  6. Learning Activities: The America's Cup Challenge. Meter Reading.

    ERIC Educational Resources Information Center

    Cronk, Rob; And Others

    1995-01-01

    Describes two learning activities: (1) high school students design and construct a wind powered monohull vessel to travel a predetermined distance in the least amount of time; and (2) sixth graders learn about energy by doing gas and electric meter reading. (Author/JOW)

  7. A Hybrid Approach to University Subject Learning Activities

    ERIC Educational Resources Information Center

    Osorio Gomez, Luz Adriana; Duart, Josep M.

    2012-01-01

    In order to get a better understanding of subject design and delivery using a hybrid approach, we have studied a hybrid learning postgraduate programme offered by the University of the Andes, Bogota, Colombia. The study analyses students' perceptions of subject design and delivery, with particular reference to learning activities and the roles of…

  8. Promoting Technology-Assisted Active Learning in Computer Science Education

    ERIC Educational Resources Information Center

    Gao, Jinzhu; Hargis, Jace

    2010-01-01

    This paper describes specific active learning strategies for teaching computer science, integrating both instructional technologies and non-technology-based strategies shown to be effective in the literature. The theoretical learning components addressed include an intentional method to help students build metacognitive abilities, as well as…

  9. Resource Letter ALIP-1: Active-Learning Instruction in Physics

    NASA Astrophysics Data System (ADS)

    Meltzer, David E.; Thornton, Ronald K.

    2012-06-01

    This Resource Letter provides a guide to the literature on research-based active-learning instruction in physics. These are instructional methods that are based on, assessed by, and validated through research on the teaching and learning of physics. They involve students in their own learning more deeply and more intensely than does traditional instruction, particularly during class time. The instructional methods and supporting body of research reviewed here offer potential for significantly improved learning in comparison to traditional lecture-based methods of college and university physics instruction. We begin with an introduction to the history of active learning in physics in the United States, and then discuss some methods for and outcomes of assessing pedagogical effectiveness. We enumerate and describe common characteristics of successful active-learning instructional strategies in physics. We then discuss a range of methods for introducing active-learning instruction in physics and provide references to those methods for which there is published documentation of student learning gains.

  10. Science: Videotapes for Inservice Training for Active Learning. VITAL Series.

    ERIC Educational Resources Information Center

    Kissock, Craig, Ed.

    This handbook, and the VITAL Science Series videotapes, contain 12 lessons that are examples of some of the many ways of organizing elementary school classrooms for science instruction. The videotapes that are available separately demonstrate full class and small group activities, the use of learning centers, cooperative learning, and outdoor…

  11. Enhanced Memory as a Common Effect of Active Learning

    ERIC Educational Resources Information Center

    Markant, Douglas B.; Ruggeri, Azzurra; Gureckis, Todd M.; Xu, Fei

    2016-01-01

    Despite widespread consensus among educators that "active learning" leads to better outcomes than comparatively passive forms of instruction, it is often unclear why these benefits arise. In this article, we review research showing that the opportunity to control the information experienced while learning leads to improved memory…

  12. Canada and the United States. Perspective. Learning Activity Packet.

    ERIC Educational Resources Information Center

    Maine Univ., Orono. New England - Atlantic Provinces - Quebec Center.

    The similarities and differences of Canada and the United States are explored in this Learning Activity Packet (LAP). Ten learning objectives are given which encourage students to examine: 1) the misconceptions Americans and Canadians have about each other and their ways of life; 2) the effect and influence of French and English exploration and…

  13. Students' Learning Activities While Studying Biological Process Diagrams

    ERIC Educational Resources Information Center

    Kragten, Marco; Admiraal, Wilfried; Rijlaarsdam, Gert

    2015-01-01

    Process diagrams describe how a system functions (e.g. photosynthesis) and are an important type of representation in Biology education. In the present study, we examined students' learning activities while studying process diagrams, related to their resulting comprehension of these diagrams. Each student completed three learning tasks. Verbal…

  14. Active Learning of Biochemistry Made Easy (for the Teacher)

    ERIC Educational Resources Information Center

    Bobich, Joseph A.

    2008-01-01

    This active learning pedagogical technique aims to improve students' learning in a two-semester, upper-division biochemistry course sequence in which the vast majority of students enrolled will continue on to medical or graduate schools. Instead of lecturing, the Instructor moves to the side of the room, thereby becoming "the guide on the side".…

  15. Holistic Instructional Activities for Students with Learning Disabilities.

    ERIC Educational Resources Information Center

    James, Giovanna; Milligan, Jerry L.

    1995-01-01

    Fourteen holistic, meaning-based reading and writing activities appropriate for students with learning disabilities are described, along with the theoretical background of the paradigm. As children experiment, approximate, and discover language naturally and socially, their immersion in authentic spoken and written language facilitates learning to…

  16. Individualized Instruction in Science, Introductory Physical Science, Learning Activity Packages.

    ERIC Educational Resources Information Center

    Kuczma, R. M.

    Learning Activity Packages (LAP) mostly relating to the Introductory Physical Science Text are presented in this manual for use in sampling a new type of instruction. The total of 14 topics are incorporated into five units: (1) introduction to individualized learning; (2) observation versus interpretation; (3) quantity of matter; (4) introduction…

  17. Learning French through Ethnolinguistic Activities and Individual Support

    ERIC Educational Resources Information Center

    Lafond, Celia; Bovey, Nadia Spang

    2013-01-01

    For the last six years, the university has been offering a Tutorial Programme for learning French, combining intensive courses and highly individualised learning activities. The programme is based on an ethnolinguistic approach and it is continuously monitored. It aims at rapid progress through contact with the local population, real-life…

  18. Enhancing learning in geosciences and water engineering via lab activities

    NASA Astrophysics Data System (ADS)

    Valyrakis, Manousos; Cheng, Ming

    2016-04-01

    This study focuses on the utilisation of lab based activities to enhance the learning experience of engineering students studying Water Engineering and Geosciences. In particular, the use of modern highly visual and tangible presentation techniques within an appropriate laboratory based space are used to introduce undergraduate students to advanced engineering concepts. A specific lab activity, namely "Flood-City", is presented as a case study to enhance the active engagement rate, improve the learning experience of the students and better achieve the intended learning objectives of the course within a broad context of the engineering and geosciences curriculum. Such activities, have been used over the last few years from the Water Engineering group @ Glasgow, with success for outreach purposes (e.g. Glasgow Science Festival and demos at the Glasgow Science Centre and Kelvingrove museum). The activity involves a specific setup of the demonstration flume in a sand-box configuration, with elements and activities designed so as to gamely the overall learning activity. Social media platforms can also be used effectively to the same goals, particularly in cases were the students already engage in these online media. To assess the effectiveness of this activity a purpose designed questionnaire is offered to the students. Specifically, the questionnaire covers several aspects that may affect student learning, performance and satisfaction, such as students' motivation, factors to effective learning (also assessed by follow-up quizzes), and methods of communication and assessment. The results, analysed to assess the effectiveness of the learning activity as the students perceive it, offer a promising potential for the use of such activities in outreach and learning.

  19. Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

    PubMed

    Li, Yang; Li, Guoqing; Wang, Zhenhao

    2015-01-01

    In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

  20. Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

    NASA Technical Reports Server (NTRS)

    Lima, Pedro; Beard, Randal

    1992-01-01

    The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a