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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. 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

  3. Geomagnetic Activity Forecasting Using Self-Learning Algorithms: Application in Space Weather Studies

    NASA Astrophysics Data System (ADS)

    Khalil, A. F.; Barakat, A. R.; McKee, M.

    2005-05-01

    The ability to forecast the geomagnetic activities is becoming more important as human activity in space becomes more prevalent. For example, early warning of geomagnetic storms could help mitigate their harmful effects on space electronics and on electrical power lines. Moreover, recently developed space weather algorithms that utilize physics-based models require future values of Kp as an input in order to forecast the ionospheric behavior. Computational learning theory and data-driven modeling techniques are new and rapidly expanding areas of research that aim at developing efficient learning algorithms. Here we compare self-learning algorithms regarding their abilities to forecast the level of geomagnetic activities, as represented by Kp. In particular, we consider the following algorithms: artificial neural networks, locally weighted projection regression, support vector machines, and relevance vector machines. Different parameters are considered such as: (1) length of forecasting time, (2) type and size of input data, and (3) training set size. These learning machines are compared regarding their generalization capabilities and structure reliabilities. The relative strengths and limitations of these algorithms will be presented.

  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. Deriving rules from activity diary data: A learning algorithm and results of computer experiments

    NASA Astrophysics Data System (ADS)

    Arentze, Theo A.; Hofman, Frank; Timmermans, Harry J. P.

    Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data.

  6. 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.

  7. Active Learning Using Hint Information.

    PubMed

    Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

    2015-08-01

    The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine. PMID:26079748

  8. Ensemble algorithms in reinforcement learning.

    PubMed

    Wiering, Marco A; van Hasselt, Hado

    2008-08-01

    This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.

  9. Ensemble algorithms in reinforcement learning.

    PubMed

    Wiering, Marco A; van Hasselt, Hado

    2008-08-01

    This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms. PMID:18632380

  10. The Dropout Learning Algorithm

    PubMed Central

    Baldi, Pierre; Sadowski, Peter

    2014-01-01

    Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations. PMID:24771879

  11. Natural gradient learning algorithms for RBF networks.

    PubMed

    Zhao, Junsheng; Wei, Haikun; Zhang, Chi; Li, Weiling; Guo, Weili; Zhang, Kanjian

    2015-02-01

    Radial basis function (RBF) networks are one of the most widely used models for function approximation and classification. There are many strange behaviors in the learning process of RBF networks, such as slow learning speed and the existence of the plateaus. The natural gradient learning method can overcome these disadvantages effectively. It can accelerate the dynamics of learning and avoid plateaus. In this letter, we assume that the probability density function (pdf) of the input and the activation function are gaussian. First, we introduce natural gradient learning to the RBF networks and give the explicit forms of the Fisher information matrix and its inverse. Second, since it is difficult to calculate the Fisher information matrix and its inverse when the numbers of the hidden units and the dimensions of the input are large, we introduce the adaptive method to the natural gradient learning algorithms. Finally, we give an explicit form of the adaptive natural gradient learning algorithm and compare it to the conventional gradient descent method. Simulations show that the proposed adaptive natural gradient method, which can avoid the plateaus effectively, has a good performance when RBF networks are used for nonlinear functions approximation. PMID:25380332

  12. 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…

  13. 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.

  14. Alternative learning algorithms for feedforward neural networks

    SciTech Connect

    Vitela, J.E.

    1996-03-01

    The efficiency of the back propagation algorithm to train feed forward multilayer neural networks has originated the erroneous belief among many neural networks users, that this is the only possible way to obtain the gradient of the error in this type of networks. The purpose of this paper is to show how alternative algorithms can be obtained within the framework of ordered partial derivatives. Two alternative forward-propagating algorithms are derived in this work which are mathematically equivalent to the BP algorithm. This systematic way of obtaining learning algorithms illustrated with this particular type of neural networks can also be used with other types such as recurrent neural networks.

  15. 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.

  16. Learning as Activity.

    ERIC Educational Resources Information Center

    Jonassen, David H.

    2002-01-01

    Integrates contemporary theories of learning into a theory of learning as activity. Explains ecological psychology, changes in understanding of learning, activity systems and activity theory (including the integration of consciousness and activity), and activity structure; and discusses learning as a cognitive and social process. (LRW)

  17. 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.

  18. 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.

  19. 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)

  20. On Learning Algorithms for Nash Equilibria

    NASA Astrophysics Data System (ADS)

    Daskalakis, Constantinos; Frongillo, Rafael; Papadimitriou, Christos H.; Pierrakos, George; Valiant, Gregory

    Can learning algorithms find a Nash equilibrium? This is a natural question for several reasons. Learning algorithms resemble the behavior of players in many naturally arising games, and thus results on the convergence or non-convergence properties of such dynamics may inform our understanding of the applicability of Nash equilibria as a plausible solution concept in some settings. A second reason for asking this question is in the hope of being able to prove an impossibility result, not dependent on complexity assumptions, for computing Nash equilibria via a restricted class of reasonable algorithms. In this work, we begin to answer this question by considering the dynamics of the standard multiplicative weights update learning algorithms (which are known to converge to a Nash equilibrium for zero-sum games). We revisit a 3×3 game defined by Shapley [10] in the 1950s in order to establish that fictitious play does not converge in general games. For this simple game, we show via a potential function argument that in a variety of settings the multiplicative updates algorithm impressively fails to find the unique Nash equilibrium, in that the cumulative distributions of players produced by learning dynamics actually drift away from the equilibrium.

  1. 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.

  2. Active learning of Pareto fronts.

    PubMed

    Campigotto, Paolo; Passerini, Andrea; Battiti, Roberto

    2014-03-01

    This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.

  3. 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.

  4. 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

  5. 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. PMID:27447253

  6. 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.

  7. 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.

  8. Ozone ensemble forecast with machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Mallet, Vivien; Stoltz, Gilles; Mauricette, Boris

    2009-03-01

    We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past observations and past model forecasts, the learning algorithms produce a weight for each model. A convex or linear combination of the model forecasts is then formed with these weights. This process is repeated for each round of forecasting and is therefore called sequential aggregation. The aggregated forecasts demonstrate good results; for instance, they always show better performance than the best model in the ensemble and they even compete against the best constant linear combination. In addition, the machine learning algorithms come with theoretical guarantees with respect to their performance, that hold for all possible sequences of observations, even nonstochastic ones. Our study also demonstrates the robustness of the methods. We therefore conclude that these aggregation methods are very relevant for operational forecasts.

  9. 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.

  10. Adaptive Batch Mode Active Learning.

    PubMed

    Chakraborty, Shayok; Balasubramanian, Vineeth; Panchanathan, Sethuraman

    2015-08-01

    Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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…

  16. The POP learning algorithms: reducing work in identifying fuzzy rules.

    PubMed

    Quek, C; Zhou, R W

    2001-12-01

    A novel fuzzy neural network, the Pseudo Outer-Product based Fuzzy Neural Network (POPFNN), and its two fuzzy-rule-identification algorithms are proposed in this paper. They are the Pseudo Outer-Product (POP) learning and the Lazy Pseudo Outer-Product (LazyPOP) leaning algorithms. These two learning algorithms are used in POPFNN to identify relevant fuzzy rules. In contrast with other rule-learning algorithms, the proposed algorithms have many advantages, such as being fast, reliable, efficient, and easy to understand. POP learning is a simple one-pass learning algorithm. It essentially performs rule-selection. Hence, it suffers from the shortcoming of having to consider all the possible rules. The second algorithm, the LazyPOP learning algorithm, truly identifies the fuzzy rules which are relevant and does not use a rule-selection method whereby irrelevant fuzzy rules are eliminated from an initial rule set. In addition, it is able to adjust the structure of the fuzzy neural network. The proposed LazyPOP learning algorithm is able to delete invalid feature inputs according to the fuzzy rules that have been identified. Extensive experimental results and discussions are presented for a detailed analysis of the proposed algorithms.

  17. Active Learning Crosses Generations.

    ERIC Educational Resources Information Center

    Woodard, Diane K.

    2002-01-01

    Describes the benefits of intergenerational programs, highlighting a child care program that offers age-appropriate and mutually beneficial activities for children and elders within a nearby retirement community. The program has adopted High/Scope's active learning approach to planning and implementing activities that involve both generations. The…

  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. 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.

  20. 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…

  1. 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.

  2. 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.

  3. Active Learning: Learning a Motor Skill Without a Coach

    PubMed Central

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

    2008-01-01

    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. PMID:18509079

  4. 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. PMID:18509079

  5. Nutrition. Learning Activity Package.

    ERIC Educational Resources Information Center

    Lee, Carolyn

    This learning activity package on nutrition 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 are…

  6. Grooming. Learning Activity Package.

    ERIC Educational Resources Information Center

    Stark, Pamela

    This learning activity package on grooming for health workers 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…

  7. 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…

  8. 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

  9. 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.

  10. 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…

  11. Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

    PubMed Central

    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. PMID:24795876

  12. Pitch-Learning Algorithm For Speech Encoders

    NASA Technical Reports Server (NTRS)

    Bhaskar, B. R. Udaya

    1988-01-01

    Adaptive algorithm detects and corrects errors in sequence of estimates of pitch period of speech. Algorithm operates in conjunction with techniques used to estimate pitch period. Used in such parametric and hybrid speech coders as linear predictive coders and adaptive predictive coders.

  13. Immune allied genetic algorithm for Bayesian network structure learning

    NASA Astrophysics Data System (ADS)

    Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

  14. 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.

  15. Robust facial expression recognition algorithm based on local metric learning

    NASA Astrophysics Data System (ADS)

    Jiang, Bin; Jia, Kebin

    2016-01-01

    In facial expression recognition tasks, different facial expressions are often confused with each other. Motivated by the fact that a learned metric can significantly improve the accuracy of classification, a facial expression recognition algorithm based on local metric learning is proposed. First, k-nearest neighbors of the given testing sample are determined from the total training data. Second, chunklets are selected from the k-nearest neighbors. Finally, the optimal transformation matrix is computed by maximizing the total variance between different chunklets and minimizing the total variance of instances in the same chunklet. The proposed algorithm can find the suitable distance metric for every testing sample and improve the performance on facial expression recognition. Furthermore, the proposed algorithm can be used for vector-based and matrix-based facial expression recognition. Experimental results demonstrate that the proposed algorithm could achieve higher recognition rates and be more robust than baseline algorithms on the JAFFE, CK, and RaFD databases.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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…

  3. ACTIVITY LEVEL AND LEARNING EFFECTIVENESS.

    ERIC Educational Resources Information Center

    SJOGREN, DOUGLAS D.; STAKE, ROBERT E.

    A STUDY OF LEARNING ACTIVITY EXPLORED (1) AN ACTIVITY-ACHIEVEMENT SCALE TO DESCRIBE THE IMPACT OF ACTIVITY ON ACHIEVEMENT AND (2) THE POSSIBLE COMPLEXITY OR DIMENSIONALITY OF THIS IMPACT. TEN GROUPS, OF 10 COLLEGE UNDERGRADUATE STUDENTS EACH, WERE SCHEDULED TO STUDY UNDER EACH OF 10 LEARNING SITUATIONS. THE SITUATIONS CONSISTED OF TWO MODES OF…

  4. Activating the Desire to Learn

    ERIC Educational Resources Information Center

    Sullo, Bob

    2007-01-01

    Wouldn't your job be easier if students were just more interested in learning? Now, here's a book that will open your eyes to where the desire to learn actually comes from and what teachers can really do to activate it. Using stories from classroom teachers, counselors, administrators, and students, Bob Sullo explains why the desire to learn is…

  5. 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.

  6. Scalable histopathological image analysis via active learning.

    PubMed

    Zhu, Yan; Zhang, Shaoting; Liu, Wei; Metaxas, Dimitris N

    2014-01-01

    Training an effective and scalable system for medical image analysis usually requires a large amount of labeled data, which incurs a tremendous annotation burden for pathologists. Recent progress in active learning can alleviate this issue, leading to a great reduction on the labeling cost without sacrificing the predicting accuracy too much. However, most existing active learning methods disregard the "structured information" that may exist in medical images (e.g., data from individual patients), and make a simplifying assumption that unlabeled data is independently and identically distributed. Both may not be suitable for real-world medical images. In this paper, we propose a novel batch-mode active learning method which explores and leverages such structured information in annotations of medical images to enforce diversity among the selected data, therefore maximizing the information gain. We formulate the active learning problem as an adaptive submodular function maximization problem subject to a partition matroid constraint, and further present an efficient greedy algorithm to achieve a good solution with a theoretically proven bound. We demonstrate the efficacy of our algorithm on thousands of histopathological images of breast microscopic tissues. PMID:25320821

  7. 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.

  8. 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…

  9. 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.

  10. Convergence of reinforcement learning algorithms and acceleration of learning

    NASA Astrophysics Data System (ADS)

    Potapov, A.; Ali, M. K.

    2003-02-01

    The techniques of reinforcement learning have been gaining increasing popularity recently. However, the question of their convergence rate is still open. We consider the problem of choosing the learning steps αn, and their relation with discount γ and exploration degree ɛ. Appropriate choices of these parameters may drastically influence the convergence rate of the techniques. From analytical examples, we conjecture optimal values of αn and then use numerical examples to verify our conjectures.

  11. 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…

  12. Floriculture. Selected Learning Activity Packages.

    ERIC Educational Resources Information Center

    Clemson Univ., SC. Vocational Education Media Center.

    This series of learning activity packages is based on a catalog of performance objectives, criterion-referenced measures, and performance guides for gardening/groundskeeping developed by the Vocational Education Consortium of States (V-TECS). Learning activity packages are presented in four areas: (1) preparation of soils and planting media, (2)…

  13. 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…

  14. 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…

  15. 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.

  16. 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…

  17. A fast learning algorithm for deep belief nets.

    PubMed

    Hinton, Geoffrey E; Osindero, Simon; Teh, Yee-Whye

    2006-07-01

    We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.

  18. 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.

  19. 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.…

  20. Self-learning metabasin escape algorithm for supercooled liquids.

    PubMed

    Cao, Penghui; Li, Minghai; Heugle, Ravi J; Park, Harold S; Lin, Xi

    2012-07-01

    A generic history-penalized metabasin escape algorithm that contains no predetermined parameters is presented in this work. The spatial location and volume of imposed penalty functions in the configurational space are determined in self-learning processes as the 3N-dimensional potential energy surface is sampled. The computational efficiency is demonstrated using a binary Lennard-Jones liquid supercooled below the glass transition temperature, which shows an O(10(3)) reduction in the quadratic scaling coefficient of the overall computational cost as compared to the previous algorithm implementation. Furthermore, the metabasin sizes of supercooled liquids are obtained as a natural consequence of determining the self-learned penalty function width distributions. In the case of a bulk binary Lennard-Jones liquid at a fixed density of 1.2, typical metabasins are found to contain about 148 particles while having a correlation length of 3.09 when the system temperature drops below the glass transition temperature.

  1. Recursive least-squares learning algorithms for neural networks

    SciTech Connect

    Lewis, P.S. ); Hwang, Jenq-Neng . Dept. of Electrical Engineering)

    1990-01-01

    This paper presents the development of a pair of recursive least squares (RLS) algorithms for online training of multilayer perceptrons, which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation, either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is in the order of (N{sup 2}), where N is the number of network parameters. This is due to the estimation of the N {times} N inverse Hessian matrix. Less computationally intensive approximations of the RLS algorithms can be easily derived by using only block diagonal elements of this matrix, thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example, RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6331). 14 refs., 3 figs.

  2. Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.

    PubMed

    Xu, Dongpo; Xia, Yili; Mandic, Danilo P

    2016-02-01

    The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks. PMID:26087504

  3. Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.

    PubMed

    Xu, Dongpo; Xia, Yili; Mandic, Danilo P

    2016-02-01

    The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.

  4. 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.

  5. 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. PMID:23140797

  6. 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. PMID:26422422

  7. 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.

  8. A novel algorithm for scalable and accurate Bayesian network learning.

    PubMed

    Brown, Laura E; Tsamardinos, Ioannis; Aliferis, Constantin F

    2004-01-01

    Bayesian Networks (BN) is a knowledge representation formalism that has been proven to be valuable in biomedicine for constructing decision support systems and for generating causal hypotheses from data. Given the emergence of datasets in medicine and biology with thousands of variables and that current algorithms do not scale more than a few hundred variables in practical domains, new efficient and accurate algorithms are needed to learn high quality BNs from data. We present a new algorithm called Max-Min Hill-Climbing (MMHC) that builds upon and improves the Sparse Candidate (SC) algorithm; a state-of-the-art algorithm that scales up to datasets involving hundreds of variables provided the generating networks are sparse. Compared to the SC, on a number of datasets from medicine and biology, (a) MMHC discovers BNs that are structurally closer to the data-generating BN, (b) the discovered networks are more probable given the data, (c) MMHC is computationally more efficient and scalable than SC, and (d) the generating networks are not required to be uniformly sparse nor is the user of MMHC required to guess correctly the network connectivity

  9. An active learning approach with uncertainty, representativeness, and diversity.

    PubMed

    He, Tianxu; Zhang, Shukui; Xin, Jie; Zhao, Pengpeng; Wu, Jian; Xian, Xuefeng; Li, Chunhua; Cui, Zhiming

    2014-01-01

    Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances' uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches. PMID:25180208

  10. Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset

    NASA Astrophysics Data System (ADS)

    Yearwood, J. L.; Kang, B. H.; Kelarev, A. V.

    The present article is devoted to experimental investigation of the performance of three machine learning algorithms for ITS dataset in their ability to achieve agreement with classes published in the biologi cal literature before. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form a Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel machine learning ap proaches to the analysis of datasets of this sort. This paper introduces a k-committees classifier and compares it with the discrete k-means and Nearest Neighbour classifiers. It turns out that all three machine learning algorithms are efficient and can be used to automate future biologically significant classifications for datasets of this kind. A simplified version of a synthetic dataset, where the k-committees classifier outperforms k-means and Nearest Neighbour classifiers, is also presented.

  11. Orthogonal least squares learning algorithm for radial basis function networks.

    PubMed

    Chen, S; Cowan, C N; Grant, P M

    1991-01-01

    The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several drawbacks, and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The authors propose an alternative learning procedure based on the orthogonal least-squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. In the algorithm, each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least-squares learning strategy provides a simple and efficient means for fitting radial basis function networks. This is illustrated using examples taken from two different signal processing applications.

  12. 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…

  13. What Is Meant by "Active Learning?"

    ERIC Educational Resources Information Center

    Petress, Ken

    2008-01-01

    This article compares and contrasts active learning and passive learning. The author describes passive learning as being dependent on a teacher imparting what is to be learned, with little student involvement. Active learning, on the other hand, is a process where students take a dynamic and energetic role in their own education, thereby making…

  14. [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…

  15. 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)

  16. A self-learning algorithm for biased molecular dynamics.

    PubMed

    Tribello, Gareth A; Ceriotti, Michele; Parrinello, Michele

    2010-10-12

    A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences. PMID:20876135

  17. Library support for problem-based learning: an algorithmic approach.

    PubMed

    Ispahany, Nighat; Torraca, Kathren; Chilov, Marina; Zimbler, Elaine R; Matsoukas, Konstantina; Allen, Tracy Y

    2007-01-01

    Academic health sciences libraries can take various approaches to support the problem-based learning component of the curriculum. This article presents one such approach taken to integrate information navigation skills into the small group discussion part of the Pathophysiology course in the second year of the Dental school curriculum. Along with presenting general resources for the course, the Library Toolkit introduced an algorithmic approach to finding answers to sample clinical case questions. While elements of Evidence-Based Practice were introduced, the emphasis was on teaching students to navigate relevant resources and apply various database search techniques to find answers to the clinical problems presented.

  18. 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.

  19. 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…

  20. Active Learning in Introductory Climatology.

    ERIC Educational Resources Information Center

    Dewey, Kenneth F.; Meyer, Steven J.

    2000-01-01

    Introduces a software package available for the climatology curriculum that determines possible climatic events according to a long-term climate history. Describes the integration of the software into the curriculum and presents examples of active learning. (Contains 19 references.) (YDS)

  1. 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…

  2. 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…

  3. Blood Pressure. Learning Activity Package.

    ERIC Educational Resources Information Center

    Hime, Kirsten

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

  4. 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…

  5. 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…

  6. Applicability of statistical learning algorithms in groundwater quality modeling

    NASA Astrophysics Data System (ADS)

    Khalil, Abedalrazq; Almasri, Mohammad N.; McKee, Mac; Kaluarachchi, Jagath J.

    2005-05-01

    Four algorithms are outlined, each of which has interesting features for predicting contaminant levels in groundwater. Artificial neural networks (ANN), support vector machines (SVM), locally weighted projection regression (LWPR), and relevance vector machines (RVM) are utilized as surrogates for a relatively complex and time-consuming mathematical model to simulate nitrate concentration in groundwater at specified receptors. Nitrates in the application reported in this paper are due to on-ground nitrogen loadings from fertilizers and manures. The practicability of the four learning machines in this work is demonstrated for an agriculture-dominated watershed where nitrate contamination of groundwater resources exceeds the maximum allowable contaminant level at many locations. Cross-validation and bootstrapping techniques are used for both training and performance evaluation. Prediction results of the four learning machines are rigorously assessed using different efficiency measures to ensure their generalization ability. Prediction results show the ability of learning machines to build accurate models with strong predictive capabilities and hence constitute a valuable means for saving effort in groundwater contamination modeling and improving model performance.

  7. 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…

  8. Active learning in the presence of unlabelable examples

    NASA Technical Reports Server (NTRS)

    Mazzoni, Dominic; Wagstaff, Kiri

    2004-01-01

    We propose a new active learning framework where the expert labeler is allowed to decline to label any example. This may be necessary because the true label is unknown or because the example belongs to a class that is not part of the real training problem. We show that within this framework, popular active learning algorithms (such as Simple) may perform worse than random selection because they make so many queries to the unlabelable class. We present a method by which any active learning algorithm can be modified to avoid unlabelable examples by training a second classifier to distinguish between the labelable and unlabelable classes. We also demonstrate the effectiveness of the method on two benchmark data sets and a real-world problem.

  9. A neural-network learning theory and a polynomial time RBF algorithm.

    PubMed

    Roy, A; Govil, S; Miranda, R

    1997-01-01

    This paper presents a new learning theory (a set of principles for brain-like learning) and a corresponding algorithm for the neural-network field. The learning theory defines computational characteristics that are much more brain-like than that of classical connectionist learning. Robust and reliable learning algorithms would result if these learning principles are followed rigorously when developing neural-network algorithms. This paper also presents a new algorithm for generating radial basis function (RBF) nets for function approximation. The design of the algorithm is based on the proposed set of learning principles. The net generated by this algorithm is not a typical RBF net, but a combination of "truncated" RBF and other types of hidden units. The algorithm uses random clustering and linear programming (LP) to design and train this "mixed" RBF net. Polynomial time complexity of the algorithm is proven and computational results are provided for the well known Mackey-Glass chaotic time series problem, the logistic map prediction problem, various neuro-control problems, and several time series forecasting problems. The algorithm can also be implemented as an online adaptive algorithm.

  10. 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.

  11. History and Evolution of Active Learning Spaces

    ERIC Educational Resources Information Center

    Beichner, Robert J.

    2014-01-01

    This chapter examines active learning spaces as they have developed over the years. Consistently well-designed classrooms can facilitate active learning even though the details of implementing pedagogies may differ.

  12. 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.

  13. 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

    2016-09-01

    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.

  14. 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…

  15. 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…

  16. Student Active Learning Methods in Physical Chemistry

    NASA Astrophysics Data System (ADS)

    Hinde, Robert J.; Kovac, Jeffrey

    2001-01-01

    We describe two strategies for implementing active learning in physical chemistry. One involves supplementing a traditional lecture course with heavily computer-based active-learning exercises carried out by cooperative groups in a department computer lab. The other uses cooperative learning almost exclusively, supplemented by occasional mini-lectures. Both approaches seemed to result in better student learning and a more positive attitude toward the subject. On the basis of our respective experiences using active learning techniques, we discuss some of the strengths of these techniques and some of the challenges we encountered using the active-learning approach in teaching physical chemistry.

  17. Overlay improvements using a real time machine learning algorithm

    NASA Astrophysics Data System (ADS)

    Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

    2014-04-01

    While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

  18. 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.

  19. 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.

  20. Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm

    SciTech Connect

    Patton, Robert M; Beckerman, Barbara G; Potok, Thomas E

    2009-01-01

    Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns in radiology reports that utilizes a genetic algorithm (GA) as the learning method. The approach described here successfully learned cue phrase patterns for two distinct classes of radiology reports. These patterns can then be used as a basis for automatically categorizing, clustering, or retrieving relevant data for the user.

  1. 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…

  2. 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

  3. 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.

  4. 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.

  5. 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…

  6. Using an improved association rules mining optimization algorithm in web-based mobile-learning system

    NASA Astrophysics Data System (ADS)

    Huang, Yin; Chen, Jianhua; Xiong, Shaojun

    2009-07-01

    Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

  7. Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks.

    PubMed

    Chen, S; Wu, Y; Luk, B L

    1999-01-01

    The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.

  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. Learning the behavior of Boolean circuits from examples using cultural algorithms

    NASA Astrophysics Data System (ADS)

    Reynolds, Robert G.; Sverdlik, William

    1993-09-01

    In this paper an approach to evolutionary learning based upon principles of cultural evolution is developed. In this dual-inheritance system, there is an evolving population of trait sequences as well as an associated belief space. The belief space is derived from the behavior of individuals and is used to actively constrain the traits acquired in future populations. Shifts in the representation of the belief space and the population are supported. The approach is used to solve several versions of the BOOLE problem; F6, F11, and F20. The results are compared with other approaches and the advantages of a dual inheritance approach using cultural algorithms is discussed.

  10. 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.

  11. 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…

  12. 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,…

  13. Active Learning in the Middle Grades

    ERIC Educational Resources Information Center

    Edwards, Susan

    2015-01-01

    What is active learning and what does it look like in the classroom? If students are participating in active learning, they are playing a more engaged role in the learning process and are not overly reliant on the teacher (Bransford, Brown, & Cocking, 2003; Petress, 2008). The purpose of this article is to propose a framework to describe and…

  14. Reinforcement learning or active inference?

    PubMed

    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.

  15. 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.

  16. 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…

  17. 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…

  18. 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.

  19. Using Teaching Teams to Encourage Active Learning

    ERIC Educational Resources Information Center

    Gueldenzoph, Lisa E.

    2007-01-01

    This article discusses the use of teaching teams to encourage active learning in a business communication class. The author offers examples of short activities that can be used to help create an active learning environment. Some of these favorite activities include homework reviews, the value line, 3-2-1 processor, and muddiest point. In each of…

  20. 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.

  1. A rank-based Prediction Algorithm of Learning User's Intention

    NASA Astrophysics Data System (ADS)

    Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing

    Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

  2. 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

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

    PubMed

    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

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

    PubMed

    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.

  5. Subsurface biological activity zone detection using genetic search algorithms

    SciTech Connect

    Mahinthakumar, G.; Gwo, J.P.; Moline, G.R.; Webb, O.F.

    1999-12-01

    Use of generic search algorithms for detection of subsurface biological activity zones (BAZ) is investigated through a series of hypothetical numerical biostimulation experiments. Continuous injection of dissolved oxygen and methane with periodically varying concentration stimulates the cometabolism of indigenous methanotropic bacteria. The observed breakthroughs of methane are used to deduce possible BAZ in the subsurface. The numerical experiments are implemented in a parallel computing environment to make possible the large number of simultaneous transport simulations required by the algorithm. The results show that genetic algorithms are very efficient in locating multiple activity zones, provided the observed signals adequately sample the BAZ.

  6. 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…

  7. 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…

  8. 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.

  9. A controllable sensor management algorithm capable of learning

    NASA Astrophysics Data System (ADS)

    Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

    2005-03-01

    Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

  10. 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. PMID:22789551

  11. 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.

  12. Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms.

    PubMed

    Karayiannis, Nicolaos B; Randolph-Gips, Mary M

    2005-03-01

    This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative fashion together with the prototypes. An error analysis provides some guidelines for selecting the parameter involved in the definition of the generalized mean in terms of the feature variances. The algorithms produced from this formulation are easy to implement and they are almost as fast as clustering algorithms relying on the Euclidean norm. An experimental evaluation on four data sets indicates that the proposed algorithms outperform consistently clustering algorithms relying on the Euclidean norm and they are strong competitors to non-Euclidean algorithms which are computationally more demanding.

  13. An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

    PubMed

    Zhang, Ye; Yu, Tenglong; Wang, Wenwu

    2014-01-01

    Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

  14. 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…

  15. 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…

  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. 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)

  18. 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…

  19. Are you bleeding? Validation of a machine-learning algorithm for determination of blood volume status: application to remote triage.

    PubMed

    Rickards, Caroline A; Vyas, Nisarg; Ryan, Kathy L; Ward, Kevin R; Andre, David; Hurst, Gennifer M; Barrera, Chelsea R; Convertino, Victor A

    2014-03-01

    Due to limited remote triage monitoring capabilities, combat medics cannot currently distinguish bleeding soldiers from those engaged in combat unless they have physical access to them. The purpose of this study was to test the hypothesis that low-level physiological signals can be used to develop a machine-learning algorithm for tracking changes in central blood volume that will subsequently distinguish central hypovolemia from physical activity. Twenty-four subjects underwent central hypovolemia via lower body negative pressure (LBNP), and a supine-cycle exercise protocol. Exercise workloads were determined by matching heart rate responses from each LBNP level. Heart rate and stroke volume (SV) were measured via Finometer. ECG, heat flux, skin temperature, galvanic skin response, and two-axis acceleration were obtained from an armband (SenseWear Pro2) and used to develop a machine-learning algorithm to predict changes in SV as an index of central blood volume under both conditions. The algorithm SV was retrospectively compared against Finometer SV. A model was developed to determine whether unknown data points could be correctly classified into these two conditions using leave-one-out cross-validation. Algorithm vs. Finometer SV values were strongly correlated for LBNP in individual subjects (mean r = 0.92; range 0.75-0.98), but only moderately correlated for exercise (mean r = 0.50; range -0.23-0.87). From the first level of LBNP/exercise, the machine-learning algorithm was able to distinguish between LBNP and exercise with high accuracy, sensitivity, and specificity (all ≥90%). In conclusion, a machine-learning algorithm developed from low-level physiological signals could reliably distinguish central hypovolemia from exercise, indicating that this device could provide battlefield remote triage capabilities. PMID:24408992

  20. 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…

  1. Unsupervised active learning based on hierarchical graph-theoretic clustering.

    PubMed

    Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

    2009-10-01

    Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples. PMID:19336318

  2. 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…

  3. Comparing Learning Performance of Students Using Algorithm Visualizations Collaboratively on Different Engagement Levels

    ERIC Educational Resources Information Center

    Laakso, Mikko-Jussi; Myller, Niko; Korhonen, Ari

    2009-01-01

    In this paper, two emerging learning and teaching methods have been studied: collaboration in concert with algorithm visualization. When visualizations have been employed in collaborative learning, collaboration introduces new challenges for the visualization tools. In addition, new theories are needed to guide the development and research of the…

  4. 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…

  5. 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.

  6. Learning evasive maneuvers using evolutionary algorithms and neural networks

    NASA Astrophysics Data System (ADS)

    Kang, Moung Hung

    In this research, evolutionary algorithms and recurrent neural networks are combined to evolve control knowledge to help pilots avoid being struck by a missile, based on a two-dimensional air combat simulation model. The recurrent neural network is used for representing the pilot's control knowledge and evolutionary algorithms (i.e., Genetic Algorithms, Evolution Strategies, and Evolutionary Programming) are used for optimizing the weights and/or topology of the recurrent neural network. The simulation model of the two-dimensional evasive maneuver problem evolved is used for evaluating the performance of the recurrent neural network. Five typical air combat conditions were selected to evaluate the performance of the recurrent neural networks evolved by the evolutionary algorithms. Analysis of Variance (ANOVA) tests and response graphs were used to analyze the results. Overall, there was little difference in the performance of the three evolutionary algorithms used to evolve the control knowledge. However, the number of generations of each algorithm required to obtain the best performance was significantly different. ES converges the fastest, followed by EP and then by GA. The recurrent neural networks evolved by the evolutionary algorithms provided better performance than the traditional recommendations for evasive maneuvers, maximum gravitational turn, for each air combat condition. Furthermore, the recommended actions of the recurrent neural networks are reasonable and can be used for pilot training.

  7. 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

  8. Neural net algorithms that learn in polynomial time from examples and queries.

    PubMed

    Baum, E B

    1991-01-01

    An algorithm which trains networks using examples and queries is proposed. In a query, the algorithm supplies a y and is told t(y) by an oracle. Queries appear to be available in practice for most problems of interest, e.g. by appeal to a human expert. The author's algorithm is proved to PAC learn in polynomial time the class of target functions defined by layered, depth two, threshold nets having n inputs connected to k hidden threshold units connected to one or more output units, provided k=/<4. While target functions and input distributions can be described for which the algorithm will fail for larger k, it appears likely to work well in practice. Tests of a variant of the algorithm have consistently and rapidly learned random nets of this type. Computational efficiency figures are given. The algorithm can also be proved to learn intersections of k half-spaces in R(n) in time polynomial in both n and k. A variant of the algorithm can learn arbitrary depth layered threshold networks with n inputs and k units in the first hidden layer in time polynomial in the larger of n and k but exponential in the smaller of the two.

  9. 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.

  10. 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. PMID:15593379

  11. 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

  12. 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.

  13. Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants

    SciTech Connect

    Parlos, A.G.; Atiya, Amir; Chong, K.T. )

    1991-11-01

    A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process.

  14. Learning by Doing: Engaging Students through Learner-Centered Activities

    ERIC Educational Resources Information Center

    Smart, Karl L.; Csapo, Nancy

    2007-01-01

    With a shift of focus from teaching to learning in higher education, teachers often look for strategies to involve students actively in the learning process, especially since numerous studies have demonstrated that a student's active involvement in the learning process enhances learning. Active learning has resulted in positive learning outcomes.…

  15. Reconstructing Causal Biological Networks through Active Learning

    PubMed Central

    Cho, Hyunghoon; Berger, Bonnie; Peng, Jian

    2016-01-01

    Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments. PMID:26930205

  16. Reconstructing Causal Biological Networks through Active Learning.

    PubMed

    Cho, Hyunghoon; Berger, Bonnie; Peng, Jian

    2016-01-01

    Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments. PMID:26930205

  17. 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.

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

    PubMed

    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

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

    PubMed

    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.

  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. 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.

  2. 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…

  3. 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

  4. Where's the Evidence that Active Learning Works?

    ERIC Educational Resources Information Center

    Michael, Joel

    2006-01-01

    Calls for reforms in the ways we teach science at all levels, and in all disciplines, are wide spread. The effectiveness of the changes being called for, employment of student-centered, active learning pedagogy, is now well supported by evidence. The relevant data have come from a number of different disciplines that include the learning sciences,…

  5. Active Affective Learning for Accelerated Schools.

    ERIC Educational Resources Information Center

    Richardson, Robert B.

    This paper provides the groundwork for Active Affective Learning and teaching adapted to the needs of the disadvantaged, at-risk students served by the Accelerated Schools Movement. One of the "golden rules" for the practice of Accelerated Learning, according to psychiatrist Georgi Lozanov, has been to maintain an "up-beat" classroom presentation…

  6. Active Learning in Applied Ethics Instruction.

    ERIC Educational Resources Information Center

    Brisin, Tom

    1995-01-01

    Describes an active learning strategy used in a college ethics course to direct students into exploring multiple perspectives in case studies. The technique expands on collaborative learning methods by adding role-playing and reflective writing. The technique has been successful in a journalism ethics course and is adaptable to any field in which…

  7. 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…

  8. 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…

  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. 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.

  11. Applying Active Learning to Assertion Classification of Concepts in Clinical Text

    PubMed Central

    Chen, Yukun; Mani, Subramani; Xu, Hua

    2012-01-01

    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. PMID:22127105

  12. 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.

  13. An Active Learning Project for Forage Courses.

    ERIC Educational Resources Information Center

    Hall, M. H.

    1989-01-01

    Presented is a successfully implemented active learning project and results of a survey to assess the success of the project. Materials and methods are discussed, and an example of one project is provided. (Author/CW)

  14. 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

  15. Learning interpretive decision algorithm for severe storm forecasting support

    SciTech Connect

    Gaffney, J.E. Jr.; Racer, I.R.

    1983-01-01

    As part of its ongoing program to develop new and better forecasting procedures and techniques, the National Weather Service has initiated an effort in interpretive processing. Investigation has begun to determine the applicability of artificial intelligence (AI)/expert system technology to interpretive processing. This paper presents an expert system algorithm that is being investigated to support the forecasting of severe thunderstorms. 14 references.

  16. 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.

  17. 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…

  18. Probabilistic tracking control for non-Gaussian stochastic process using novel iterative learning algorithms

    NASA Astrophysics Data System (ADS)

    Yi, Yang; Sun, ChangYin; Guo, Lei

    2013-07-01

    A new generalised iterative learning algorithm is presented for complex dynamic non-Gaussian stochastic processes. After designed neural networks are used to approximate the output probability density function (PDF) of the stochastic system in the repetitive processes or the batch processes, the complex probabilistic tracking control to the output PDF is simplified into a parameter tuning problem between two adjacent repetitive processes. Under this framework, this article studies a novel model free iterative learning control problem and proposes a convex optimisation algorithm based on a set of designed linear matrix inequalities and L 1 optimisation index. It is noted that such an algorithm can improve the tracking performance and robustness for the closed-loop PDF control. A simulated example is given, which effectively demonstrates the use of the proposed control algorithm.

  19. 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.

  20. 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 through online…

  1. Learning Activism, Acting with Phronesis

    ERIC Educational Resources Information Center

    Lee, Yew-Jin

    2015-01-01

    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…

  2. 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.

  3. 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

  4. Asymmetric variate generation via a parameterless dual neural learning algorithm.

    PubMed

    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.

  5. 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.

  6. Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information.

    PubMed

    Xu, Lu; Huang, Defeng David; Guo, Yingjie Jay

    2015-12-01

    In this paper, we propose a new blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type of extra information, the input decision information obtained from the input of the equalizer, to mitigate the influence of the nonlinear equalizer structure on parameters learning, thereby leading to improved convergence performance. We prove that, with the input decision information, a desirable convergence capability that the output symbol error rate (SER) is always less than the input SER if the input SER is below a threshold, can be achieved. Then, the BG soft-switching technique is employed to combine the merits of both input and output decision information, where the former is used to guarantee SER convergence and the latter is to improve SER performance. Simulation results show that the proposed algorithm outperforms conventional blind learning algorithms, such as stochastic quadratic distance and dual mode constant modulus algorithm, in terms of both convergence performance and SER performance, for nonlinear equalization.

  7. Correlates of reward-predictive value in learning-related hippocampal neural activity

    PubMed Central

    Okatan, Murat

    2009-01-01

    Temporal difference learning (TD) is a popular algorithm in machine learning. Two learning signals that are derived from this algorithm, the predictive value and the prediction error, have been shown to explain changes in neural activity and behavior during learning across species. Here, the predictive value signal is used to explain the time course of learning-related changes in the activity of hippocampal neurons in monkeys performing an associative learning task. The TD algorithm serves as the centerpiece of a joint probability model for the learning-related neural activity and the behavioral responses recorded during the task. The neural component of the model consists of spiking neurons that compete and learn the reward-predictive value of task-relevant input signals. The predictive-value signaled by these neurons influences the behavioral response generated by a stochastic decision stage, which constitutes the behavioral component of the model. It is shown that the time course of the changes in neural activity and behavioral performance generated by the model exhibits key features of the experimental data. The results suggest that information about correct associations may be expressed in the hippocampus before it is detected in the behavior of a subject. In this way, the hippocampus may be among the earliest brain areas to express learning and drive the behavioral changes associated with learning. Correlates of reward-predictive value may be expressed in the hippocampus through rate remapping within spatial memory representations, they may represent reward-related aspects of a declarative or explicit relational memory representation of task contingencies, or they may correspond to reward-related components of episodic memory representations. These potential functions are discussed in connection with hippocampal cell assembly sequences and their reverse reactivation during the awake state. The results provide further support for the proposal that neural

  8. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

    PubMed Central

    Kang, Dongwoo; Dey, Damini; Slomka, Piotr J.; Arsanjani, Reza; Nakazato, Ryo; Ko, Hyunsuk; Berman, Daniel S.; Li, Debiao; Kuo, C.-C. Jay

    2015-01-01

    Abstract. Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA. PMID:26158081

  9. An efficient algorithm to identify coordinately activated transcription factors.

    PubMed

    Hu, Haiyan

    2010-03-01

    Identification of transcription factor (TF) activities associated with a certain physiological/experimental condition is one of the preliminary steps to reconstruct transcriptional regulatory networks and to identify signal transduction pathways. TF activities are often indicated by the activities of its target genes. Existing studies on identifying TF activities through target genes usually assume the equivalence between co-regulation and co-expression. However, genes with correlated expression profiles may not be co-regulated. In the mean time, although multiple TFs can be activated coordinately, there is a lack of efficient methods to identify coordinately activated TFs. In this paper, we propose an efficient algorithm embedding a dynamic programming procedure to identify a subset of TFs that are potentially coordinately activated under a given condition by utilizing ranked lists of differentially expressed target genes. Applying our algorithm to microarray expression data sets for a number of diseases, our approach found subsets of TFs that are highly likely associated with the given disease processes. PMID:20060041

  10. 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. PMID:23335580

  11. 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. PMID:24781953

  12. 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.

  13. 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. PMID:26843855

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

    PubMed Central

    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. PMID:26843855

  15. An algorithm of multi-structure based on Riemannian manifold learning

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Bi, Du-Yan; Xiong, Lei

    2010-08-01

    Riemannian Manifold Learning (RML) is a global algorithm proposed recently, so it can't preserve the local geometry property of neighboring data well. An algorithm of multi-structure based on RML is proposed in order to solve the problem. In the algorithm, all points were projected by PCA firstly so as to the extracted character is irrelevant, then constructed a neighbor graph. The most important step was that all data points were classified to two parts, for the k - NN of a base point, it adopted a weight which can preserve local property of the base point and neighboring nods to get the low-dimensional embedding coordinates. As for the other points, it still used the RML algorithm. Thus the new algorithm can both preserve the metrics at all scales and keep the geometrical property of local neighbor to the maximum. Experimental results on synthetic data and MNIST data set demonstrate that the new algorithm can reflect the intrinsic property better than the other manifold learning algorithms.

  16. 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

  17. 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. PMID:25208128

  18. An incremental learning algorithm based on Support Vector Machine for pattern recognition

    NASA Astrophysics Data System (ADS)

    Zou, Lamei; Zhang, Tianxu; Cao, Zhiguo

    2009-10-01

    With the advent of information age, especially with the rapid development of network, "information explosion" problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training process.

  19. 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.

  20. Bioinformatics algorithm based on a parallel implementation of a machine learning approach using transducers

    PubMed Central

    Roche-Lima, Abiel; Thulasiram, Ruppa K.

    2016-01-01

    Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.

  1. 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…

  2. 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…

  3. 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…

  4. 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 Ready for Algebra"; (3)…

  5. Grooming. Instructor's Packet. Learning Activity Package.

    ERIC Educational Resources Information Center

    Stark, Pamela

    This instructor's packet accompanies the learning activity package (LAP) on grooming. 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, an additional resources list, and student completion cards to issue to students as an…

  6. 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…

  7. Classifying Volcanic Activity Using an Empirical Decision Making Algorithm

    NASA Astrophysics Data System (ADS)

    Junek, W. N.; Jones, W. L.; Woods, M. T.

    2012-12-01

    Detection and classification of developing volcanic activity is vital to eruption forecasting. Timely information regarding an impending eruption would aid civil authorities in determining the proper response to a developing crisis. In this presentation, volcanic activity is characterized using an event tree classifier and a suite of empirical statistical models derived through logistic regression. Forecasts are reported in terms of the United States Geological Survey (USGS) volcano alert level system. The algorithm employs multidisciplinary data (e.g., seismic, GPS, InSAR) acquired by various volcano monitoring systems and source modeling information to forecast the likelihood that an eruption, with a volcanic explosivity index (VEI) > 1, will occur within a quantitatively constrained area. Logistic models are constructed from a sparse and geographically diverse dataset assembled from a collection of historic volcanic unrest episodes. Bootstrapping techniques are applied to the training data to allow for the estimation of robust logistic model coefficients. Cross validation produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78-0.81, which indicates the algorithm has good predictive capabilities. The ROC curves also allowed for the determination of a false positive rate and optimum detection for each stage of the algorithm. Forecasts for historic volcanic unrest episodes in North America and Iceland were computed and are consistent with the actual outcome of the events.

  8. Active and passive contributions to spatial learning.

    PubMed

    Chrastil, Elizabeth R; Warren, William H

    2012-02-01

    It seems intuitively obvious that active exploration of a new environment will lead to better spatial learning than will passive exposure. However, the literature on this issue is decidedly mixed-in part, because the concept itself is not well defined. We identify five potential components of active spatial learning and review the evidence regarding their role in the acquisition of landmark, route, and survey knowledge. We find that (1) idiothetic information in walking contributes to metric survey knowledge, (2) there is little evidence as yet that decision making during exploration contributes to route or survey knowledge, (3) attention to place-action associations and relevant spatial relations contributes to route and survey knowledge, although landmarks and boundaries appear to be learned without effort, (4) route and survey information are differentially encoded in subunits of working memory, and (5) there is preliminary evidence that mental manipulation of such properties facilitates spatial learning. Idiothetic information appears to be necessary to reveal the influence of attention and, possibly, decision making in survey learning, which may explain the mixed results in desktop virtual reality. Thus, there is indeed an active advantage in spatial learning, which manifests itself in the task-dependent acquisition of route and survey knowledge.

  9. 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…

  10. 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...

  11. 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

  12. 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.…

  13. 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

  14. Learning assignment order of instances for the constrained K-means clustering algorithm.

    PubMed

    Hong, Yi; Kwong, Sam

    2009-04-01

    The sensitivity of the constrained K-means clustering algorithm (Cop-Kmeans) to the assignment order of instances is studied, and a novel assignment order learning method for Cop-Kmeans, termed as clustering Uncertainty-based Assignment order Learning Algorithm (UALA), is proposed in this paper. The main idea of UALA is to rank all instances in the data set according to their clustering uncertainties calculated by using the ensembles of multiple clustering algorithms. Experimental results on several real data sets with artificial instance-level constraints demonstrate that UALA can identify a good assignment order of instances for Cop-Kmeans. In addition, the effects of ensemble sizes on the performance of UALA are analyzed, and the generalization property of Cop-Kmeans is also studied.

  15. 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).

  16. Bayesian network structure learning based on the chaotic particle swarm optimization algorithm.

    PubMed

    Zhang, Q; Li, Z; Zhou, C J; Wei, X P

    2013-01-01

    The Bayesian network (BN) is a knowledge representation form, which has been proven to be valuable in the gene regulatory network reconstruction because of its capability of capturing causal relationships between genes. Learning BN structures from a database is a nondeterministic polynomial time (NP)-hard problem that remains one of the most exciting challenges in machine learning. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, the classical K2 algorithm is the most successful. Nonetheless, the performance of the K2 algorithm is greatly affected by a prior ordering of input nodes. The proposed method in this paper is based on the chaotic particle swarm optimization (CPSO) and the K2 algorithm. Because the PSO algorithm completely entraps the local minimum in later evolutions, we combined the PSO algorithm with the chaos theory, which has the properties of ergodicity, randomness, and regularity. Experimental results show that the proposed method can improve the convergence rate of particles and identify networks more efficiently and accurately. PMID:24222226

  17. Autonomous Motion Learning for Intra-Vehicular Activity Space Robot

    NASA Astrophysics Data System (ADS)

    Watanabe, Yutaka; Yairi, Takehisa; Machida, Kazuo

    Space robots will be needed in the future space missions. So far, many types of space robots have been developed, but in particular, Intra-Vehicular Activity (IVA) space robots that support human activities should be developed to reduce human-risks in space. In this paper, we study the motion learning method of an IVA space robot with the multi-link mechanism. The advantage point is that this space robot moves using reaction force of the multi-link mechanism and contact forces from the wall as space walking of an astronaut, not to use a propulsion. The control approach is determined based on a reinforcement learning with the actor-critic algorithm. We demonstrate to clear effectiveness of this approach using a 5-link space robot model by simulation. First, we simulate that a space robot learn the motion control including contact phase in two dimensional case. Next, we simulate that a space robot learn the motion control changing base attitude in three dimensional case.

  18. 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

  19. Learning plan applicability through active mental entities

    NASA Astrophysics Data System (ADS)

    Baroni, Pietro; Fogli, Daniela; Guida, Giovanni

    1999-03-01

    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.

  20. Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

    NASA Astrophysics Data System (ADS)

    Gao, Wei; Zhu, Linli; Wang, Kaiyun

    2015-12-01

    Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

  1. 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.

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

    PubMed

    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

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

    PubMed

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

    2016-09-22

    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.

  4. 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.

  5. 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; 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 plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

  6. Learning to control the program evolution process with cultural algorithms

    PubMed

    Zannoni; Reynolds

    1997-01-01

    Traditional software engineering dictates the use of modular and structured programming and top-down stepwise refinement techniques that reduce the amount of variability arising in the development process by establishing standard procedures to be followed while writing software. This focusing leads to reduced variability in the resulting products, due to the use of standardized constructs. Genetic programming (GP) performs heuristic search in the space of programs. Programs produced through the GP paradigm emerge as the result of simulated evolution and are built through a bottom-up process, incrementally augmenting their functionality until a satisfactory level of performance is reached. Can we automatically extract knowledge from the GP programming process that can be useful to focus the search and reduce product variability, thus leading to a more effective use of the available resources? An answer to this question is investigated with the aid of cultural algorithms. A new system, cultural algorithms with genetic programming (CAGP), is presented. The system has two levels. The first is the pool of genetic programs (population level), and the second is a knowledge repository (belief set) that is built during the GP run and is used to guide the search process. The microevolution within the population brings about potentially meaningful characteristics of the programs for the achievement of the given task, such as properties exhibited by the best performers in the population. CAGP extracts these features and represents them as the set of the current beliefs. Beliefs correspond to constraints that all the genetic operators and programs must follow. Interaction between the two levels occurs in one direction through the extraction process and, in the other, through the modulation of an individual's program parameters according to which, and how many, of the constraints it follows. CAGP is applied to solve an instance of the symbolic regression problem, in which a

  7. 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…

  8. 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/.

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

    PubMed

    Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; 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

  10. 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

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

    PubMed

    Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; 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.

  12. 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

  13. 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

  14. Deep learning algorithms for detecting explosive hazards in ground penetrating radar data

    NASA Astrophysics Data System (ADS)

    Besaw, Lance E.; Stimac, Philip J.

    2014-05-01

    Buried explosive hazards (BEHs) have been, and continue to be, one of the most deadly threats in modern conflicts. Current handheld sensors rely on a highly trained operator for them to be effective in detecting BEHs. New algorithms are needed to reduce the burden on the operator and improve the performance of handheld BEH detectors. Traditional anomaly detection and discrimination algorithms use "hand-engineered" feature extraction techniques to characterize and classify threats. In this work we use a Deep Belief Network (DBN) to transcend the traditional approaches of BEH detection (e.g., principal component analysis and real-time novelty detection techniques). DBNs are pretrained using an unsupervised learning algorithm to generate compressed representations of unlabeled input data and form feature detectors. They are then fine-tuned using a supervised learning algorithm to form a predictive model. Using ground penetrating radar (GPR) data collected by a robotic cart swinging a handheld detector, our research demonstrates that relatively small DBNs can learn to model GPR background signals and detect BEHs with an acceptable false alarm rate (FAR). In this work, our DBNs achieved 91% probability of detection (Pd) with 1.4 false alarms per square meter when evaluated on anti-tank and anti-personnel targets at temperate and arid test sites. This research demonstrates that DBNs are a viable approach to detect and classify BEHs.

  15. Learning tensegrity locomotion using open-loop control signals and coevolutionary algorithms.

    PubMed

    Iscen, Atil; Caluwaerts, Ken; Bruce, Jonathan; Agogino, Adrian; SunSpiral, Vytas; Tumer, Kagan

    2015-01-01

    Soft robots offer many advantages over traditional rigid robots. However, soft robots can be difficult to control with standard control methods. Fortunately, evolutionary algorithms can offer an elegant solution to this problem. Instead of creating controls to handle the intricate dynamics of these robots, we can simply evolve the controls using a simulation to provide an evaluation function. In this article, we show how such a control paradigm can be applied to an emerging field within soft robotics: robots based on tensegrity structures. We take the model of the Spherical Underactuated Planetary Exploration Robot ball (SUPERball), an icosahedron tensegrity robot under production at NASA Ames Research Center, develop a rolling locomotion algorithm, and study the learned behavior using an accurate model of the SUPERball simulated in the NASA Tensegrity Robotics Toolkit. We first present the historical-average fitness-shaping algorithm for coevolutionary algorithms to speed up learning while favoring robustness over optimality. Second, we use a distributed control approach by coevolving open-loop control signals for each controller. Being simple and distributed, open-loop controllers can be readily implemented on SUPERball hardware without the need for sensor information or precise coordination. We analyze signals of different complexities and frequencies. Among the learned policies, we take one of the best and use it to analyze different aspects of the rolling gait, such as lengths, tensions, and energy consumption. We also discuss the correlation between the signals controlling different parts of the tensegrity robot. PMID:25951199

  16. Choosing feature selection and learning algorithms in QSAR.

    PubMed

    Eklund, Martin; Norinder, Ulf; Boyer, Scott; Carlsson, Lars

    2014-03-24

    Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.

  17. Personalized tuning of a reinforcement learning control algorithm for glucose regulation.

    PubMed

    Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula G

    2013-01-01

    Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm. PMID:24110480

  18. Learning Outcomes between Socioscientific Issues-Based Learning and Conventional Learning Activities

    ERIC Educational Resources Information Center

    Wongsri, Piyaluk; Nuangchalerm, Prasart

    2010-01-01

    Problem statement: Socioscientific issues-based learning activity is essential for scientific reasoning skills and it could be used for analyzing problems be applied to each situation for more successful and suitable. The purposes of this research aimed to compare learning achievement, analytical thinking and moral reasoning of seventh grade…

  19. Cooperative Learning Activities for Language Arts: Challenging.

    ERIC Educational Resources Information Center

    Jasmine, Grace; Nader, Lillian

    Designed to introduce teachers to cooperative learning techniques in the language arts, this book presents 24 activity units--12 on outer space (including the exploration of space and its many adventures), and 12 on "inner space" (the mind, the emotions, and the brain). After an introduction and overview, the first section of the book gives…

  20. 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…

  1. 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…

  2. Active Learning in the Physics Classroom

    NASA Astrophysics Data System (ADS)

    Naron, Carol

    Many students enter physics classes filled with misconceptions about physics concepts. Students tend to retain these misconceptions into their adult lives, even after physics instruction. Constructivist researchers have found that students gain understanding through their experiences. Researchers have also found that active learning practices increase conceptual understanding of introductory physics students. This project study sought to examine whether incorporating active learning practices in an advanced placement physics classroom increased conceptual understanding as measured by the force concept inventory (FCI). Physics students at the study site were given the FCI as both a pre- and posttest. Test data were analyzed using two different methods---a repeated-measures t test and the Hake gain method. The results of this research project showed that test score gains were statistically significant, as measured by the t test. The Hake gain results indicated a low (22.5%) gain for the class. The resulting project was a curriculum plan for teaching the mechanics portion of Advanced Placement (AP) physics B as well as several active learning classroom practices supported by the research. This project will allow AP physics teachers an opportunity to improve their curricular practices. Locally, the results of this project study showed that research participants gained understanding of physics concepts. Social change may occur as teachers implement active learning strategies, thus creating improved student understanding of physics concepts.

  3. 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…

  4. 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…

  5. Temperature, Pulse, and Respiration. Learning Activity Package.

    ERIC Educational Resources Information Center

    Runge, Lillian

    This learning activity package on temperature, pulse, and respiration 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…

  6. Teaching Research Methodology through Active Learning

    ERIC Educational Resources Information Center

    Lundahl, Brad W.

    2008-01-01

    To complement traditional learning activities in a masters-level research methodology course, social work students worked on a formal research project which involved: designing the study, constructing measures, selecting a sampling strategy, collecting data, reducing and analyzing data, and finally interpreting and communicating the results. The…

  7. 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…

  8. 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…

  9. 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 Comparison of the 'Enlightened' Ideas of…

  10. 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 1940";…

  11. Learning about Outdoor Education through Authentic Activity

    ERIC Educational Resources Information Center

    Moffett, Pamela

    2012-01-01

    The potential, for the learner, of a maths trail was documented in MT219. Here, the focus is on the planning element of such an event from the perspective of a group of student teachers. Personal reactions, and insights are used to demonstrate that "real, and authentic, learning" takes place for all those involved in the activity.

  12. Amazing Social Studies Activities: Participatory Learning Models

    ERIC Educational Resources Information Center

    Fisher, Mercedes M.

    2004-01-01

    Teachers are responsible for delivering, selecting, and implementing learning activities for their classrooms. They must consider the best approaches to engage their students as well as to meet the school's standards in instruction. Here is a practical how-to book to supplement the social studies curriculum. It places at the teacher's disposal,…

  13. 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…

  14. 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…

  15. Handwashing Technique. Instructor's Packet. Learning Activity Package.

    ERIC Educational Resources Information Center

    Stark, Pamela

    This instructor's packet accompanies the learning activity package (LAP) on handwashing. 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, a student performance checklist, an additional resources list, and student completion cards to issue to…

  16. [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. PMID:26672281

  17. Learning Outcomes of Project-Based and Inquiry-Based Learning Activities

    ERIC Educational Resources Information Center

    Panasan, Mookdaporn; Nuangchalerm, Prasart

    2010-01-01

    Problem statement: Organization of science learning activities is necessary to rely on various methods of organization of learning and to be appropriate to learners. Organization of project-based learning activities and inquiry-based learning activities are teaching methods which can help students understand scientific knowledge. It would be more…

  18. 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.

  19. 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. PMID:25725347

  20. DSP-based on-line NMR spectroscopy using an anti-Hebbian learning algorithm

    SciTech Connect

    Razazian, K.; Dieckman, S.L.; Raptis, A.C.; Bobis, J.P. |

    1995-07-01

    This paper describes a nuclear magnetic resonance (NMR) system that uses an adaptive algorithm to carry out real-time NMR spectroscopy. The system employs a digital signal processor (DSP) chip to regulate the transmitted and received signal together with spectral analysis of the received signal to determine free induction decay (FID). To implement such a signal-processing routine for detection of the desired signal, an adaptive line enhancer filter that uses an anti-Hebbian learning algorithm is applied to the FID spectra. The results indicate that the adaptive filter can be a reliable technique for on-line spectroscopy study.

  1. Impedance learning for robotic contact tasks using natural actor-critic algorithm.

    PubMed

    Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul

    2010-04-01

    Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment. PMID:19696001

  2. Impedance learning for robotic contact tasks using natural actor-critic algorithm.

    PubMed

    Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul

    2010-04-01

    Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.

  3. 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.

  4. 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. PMID:24488868

  5. Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Nguyen, Tan H.; Sridharan, Shamira; Macias, Virgilia; Balla, Andre K.; Do, Minh N.; Popescu, Gabriel

    2015-03-01

    We report, for the first time, the use of Quantitative Phase Imaging (QPI) images to perform automatic prostate cancer diagnosis. A machine learning algorithm is implemented to learn textural behaviors of prostate samples imaged under QPI and produce labeled maps of different regions for testing biopsies (e.g. gland, stroma, lumen etc.). From these maps, morphological and textural features are calculated to predict outcomes of the testing samples. Current performance is reported on a dataset of more than 300 cores of various diagnosis results.

  6. 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.

  7. 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)…

  8. 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…

  9. 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.

  10. A hybrid manifold learning algorithm for the diagnosis and prognostication of Alzheimer’s disease

    PubMed Central

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

    2015-01-01

    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. PMID:26958180

  11. An RSVM based two-teachers-one-student semi-supervised learning algorithm.

    PubMed

    Chang, Chien-Chung; Pao, Hsing-Kuo; Lee, Yuh-Jye

    2012-01-01

    Based on the reduced SVM, we propose a multi-view algorithm, two-teachers-one-student, for semi-supervised learning. With RSVM, different from typical multi-view methods, reduced sets suggest different views in the represented kernel feature space rather than in the input space. No label information is necessary when we select reduced sets, and this makes applying RSVM to SSL possible. Our algorithm blends the concepts of co-training and consensus training. Through co-training, the classifiers generated by two views can "teach" the third classifier from the remaining view to learn, and this process is performed for each choice of teachers-student combination. By consensus training, predictions from more than one view can give us higher confidence for labeling unlabeled data. The results show that the proposed 2T1S achieves high cross-validation accuracy, even compared to the training with all the label information available.

  12. 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

  13. 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

  14. 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.

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

    PubMed

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

    2015-01-01

    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 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

  17. An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics

    NASA Astrophysics Data System (ADS)

    Valentine, Andrew; Kalnins, Lara

    2016-05-01

    "Learning algorithms" are a class of computational tool designed to infer information from a data set, and then apply that information predictively. They are particularly well suited to complex pattern recognition, or to situations where a mathematical relationship needs to be modelled but where the underlying processes are not well understood, are too expensive to compute, or where signals are over-printed by other effects. If a representative set of examples of the relationship can be constructed, a learning algorithm can assimilate its behaviour, and may then serve as an efficient, approximate computational implementation thereof. A wide range of applications in geomorphometry and Earth surface dynamics may be envisaged, ranging from classification of landforms through to prediction of erosion characteristics given input forces. Here, we provide a practical overview of the various approaches that lie within this general framework, review existing uses in geomorphology and related applications, and discuss some of the factors that determine whether a learning algorithm approach is suited to any given problem.

  18. Development of a general learning algorithm with applications in nuclear reactor systems

    SciTech Connect

    Brittain, C.R.; Otaduy, P.J.; Perez, R.B.

    1989-12-01

    The objective of this study was development of a generalized learning algorithm that can learn to predict a particular feature of a process by observation of a set of representative input examples. The algorithm uses pattern matching and statistical analysis techniques to find a functional relationship between descriptive attributes of the input examples and the feature to be predicted. The algorithm was tested by applying it to a set of examples consisting of performance descriptions for 277 fuel cycles of Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). The program learned to predict the critical rod position for the HFIR from core configuration data prior to reactor startup. The functional relationship bases its predictions on initial core reactivity, the number of certain targets placed in the center of the reactor, and the total exposure of the control plates. Twelve characteristic fuel cycle clusters were identified. Nine fuel cycles were diagnosed as having noisy data, and one could not be predicted by the functional relationship. 13 refs., 6 figs.

  19. 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.

  20. Workshop on active learning: two examples

    NASA Astrophysics Data System (ADS)

    Ben Lakhdar, Zohra; Lahmar, Souad; Lakshminarayanan, Vasudevan

    2014-07-01

    Optics is an enabling science that has far ranging importance in many diverse fields. However, many students do not find it to be of great interest. A solution to this problem is to train teachers in active learning methodologies so that the subject matter can be presented to generate student interest. We describe a workshop to present an example of an active learning process in Optics developed for training of teachers in developing countries (a UNESCO project) and will focus on 2 two different activities: 1. Interference and diffraction is considered by students as being very hard to understand and is taught in most developing countries as purely theoretical with almost no experiments. Simple experiments to enhance the conceptual understanding of these wave phenomena will be presented and 2. Image formation by the eye. Here we will discuss myopia, hyperopia and astigmatism as well as accommodation. In this module we will discuss image. The objective of the workshop will be to provide an experience of the use of the active learning method in optics including the use of experiments, mind's on and hands-on exercises, group and class discussions

  1. 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. PMID:22394690

  2. 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. PMID:26872778

  3. 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.

  4. Autoclassification of the Variable 3XMM Sources Using the Random Forest Machine Learning Algorithm

    NASA Astrophysics Data System (ADS)

    Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.

    2015-11-01

    In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.

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

    PubMed

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

    2015-01-01

    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

  6. 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

  7. 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.

  8. 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.

  9. 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)

  10. 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.

  11. 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),…

  12. 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…

  13. 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…

  14. Active Learning with "Jeopardy": Students Ask the Questions

    ERIC Educational Resources Information Center

    Benek-Rivera, Joan; Mathews, Vinitia E.

    2004-01-01

    Nontraditional instructional methods facilitate active learning by students. The "Jeopardy" exercise outlined in this article is based on the popular television game show and is presented as an active learning technique designed to (a) motivate students to actively participate in class and assume more responsibility for learning, (b) provide an…

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

    PubMed Central

    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. PMID:26366164

  17. 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. PMID:26366164

  18. Performance comparison of multi-label learning algorithms on clinical data for chronic diseases.

    PubMed

    Zufferey, Damien; Hofer, Thomas; Hennebert, Jean; Schumacher, Michael; Ingold, Rolf; Bromuri, Stefano

    2015-10-01

    We are motivated by the issue of classifying diseases of chronically ill patients to assist physicians in their everyday work. Our goal is to provide a performance comparison of state-of-the-art multi-label learning algorithms for the analysis of multivariate sequential clinical data from medical records of patients affected by chronic diseases. As a matter of fact, the multi-label learning approach appears to be a good candidate for modeling overlapped medical conditions, specific to chronically ill patients. With the availability of such comparison study, the evaluation of new algorithms should be enhanced. According to the method, we choose a summary statistics approach for the processing of the sequential clinical data, so that the extracted features maintain an interpretable link to their corresponding medical records. The publicly available MIMIC-II dataset, which contains more than 19,000 patients with chronic diseases, is used in this study. For the comparison we selected the following multi-label algorithms: ML-kNN, AdaBoostMH, binary relevance, classifier chains, HOMER and RAkEL. Regarding the results, binary relevance approaches, despite their elementary design and their independence assumption concerning the chronic illnesses, perform optimally in most scenarios, in particular for the detection of relevant diseases. In addition, binary relevance approaches scale up to large dataset and are easy to learn. However, the RAkEL algorithm, despite its scalability problems when it is confronted to large dataset, performs well in the scenario which consists of the ranking of the labels according to the dominant disease of the patient.

  19. Learning algorithms for both real-time detection of solder shorts and for SPC measurement correction using cross-sectional x-ray images of PCBA solder joints

    NASA Astrophysics Data System (ADS)

    Roder, Paul A.

    1994-03-01

    Learning algorithms are introduced for use in the inspection of cross-sectional X-ray images of solder joints. These learning algorithms improve measurement accuracy by accounting for localized shading effects that can occur when inspecting double- sided printed circuit board assemblies. Two specific examples are discussed. The first is an algorithm for detection of solder short defects. The second algorithm utilizes learning to generate more accurate statistical process control measurements.

  20. Active Learning in ASTR 101 Lectures

    NASA Astrophysics Data System (ADS)

    Deming, Grace L.

    1998-12-01

    The lecture is the most common teaching method used at colleges and universities, but does this format facilitate student learning? Lectures can be brilliantly delivered, but they are received by a passive audience. As time passes during a lecture, student attention and effective notetaking diminish. Many students become more interested in a subject and retain information longer in courses that rely on active rather than passive teaching methods. Interactive teaching strategies such as the think-pair-share-(write), the 3-minute paper, and the misconception confrontation can be used to actively engage students during lecture. As a cooperative learning strategy, the think-pair-share-(write) technique requires active discussion by everyone in the class. The "write" component structures individual accountability into the activity. The 3-minute paper is an expansion of the standard 1-minute paper feedback technique, but is required of all students rather than voluntary or anonymous. The misconception confrontation technique allows students to focus on how their pre- conceived notions differ from the scientific explanation. These techniques can be easily adopted by anyone currently using a standard lecture format for introductory astronomy. The necessary components are a commitment by the instructor to require active participation by all students and a willingness to try new teaching methods.

  1. 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…

  2. 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…

  3. 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…

  4. 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…

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

    PubMed Central

    An, Mingqiang

    2016-01-01

    Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM) is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and K Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost. PMID:27660645

  10. An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

    PubMed Central

    An, Mingqiang

    2016-01-01

    Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM) is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and K Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.

  11. Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms.

    PubMed

    Shad, Rouzbeh; Shad, Arefeh

    2012-10-01

    Modeling the impact of air pollution is one of the most important approaches for managing damages to the ecosystem. This problem can be solved by sensing and modeling uncertain spatial behaviors, defining topological rules, and using inference and learning capabilities in a spatial reasoning system. Reasoning, which is the main component of such complex systems, requires that proper rules be defined through expert judgments in the knowledge-based part. Use of genetic fuzzy capabilities enables the algorithm to learn and be tuned to proper rules in a flexible manner and increases the preciseness and robustness of operations. The main objective of this paper was to design and evaluate a spatial genetic fuzzy system, with the goal of assessing environmental risks of air pollution due to oil well fires during the Persian Gulf War. Dynamic areas were extracted and monitored through images from NOAA, and the data were stored in an efficient spatial database. Initial spatial knowledge was determined by expert consideration of the application characteristics, and the inference engine was performed with genetic learning (GL) algorithms. Finally, GL (0.7 and 0.03), GL (0.7 and 0.08), GL (0.98 and 0.03), GL (0.98 and 0.08), and Cordon learning methods were evaluated with test and training data related to samples extracted from Landsat thematic mapper satellite images. Results of the implementation showed that GL (0.98, 0.03) was more precise than the other methods for learning and tuning rules in the concerned application. PMID:22068317

  12. The New Science of Learning: Active Learning, Metacognition, and Transfer of Knowledge in E-Learning Applications

    ERIC Educational Resources Information Center

    Huffaker, David A.; Calvert, Sandra L.

    2003-01-01

    This article examines the key concepts of active learning, metacognition, and transfer of knowledge, as put forth by the National Research Council's approach to the new science of learning, in relation to ways that E-Learning applications might improve learning both inside and outside the classroom. Several initiatives are highlighted to…

  13. Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

    PubMed Central

    Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

    2011-01-01

    Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373

  14. Active learning not associated with student learning in a random sample of college biology courses.

    PubMed

    Andrews, T M; Leonard, M J; Colgrove, C A; Kalinowski, S T

    2011-01-01

    Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning.

  15. A Fast Algorithm for Learning Overcomplete Dictionary for Sparse Representation Based on Proximal Operators.

    PubMed

    Li, Zhenni; Ding, Shuxue; Li, Yujie

    2015-09-01

    We present a fast, efficient algorithm for learning an overcomplete dictionary for sparse representation of signals. The whole problem is considered as a minimization of the approximation error function with a coherence penalty for the dictionary atoms and with the sparsity regularization of the coefficient matrix. Because the problem is nonconvex and nonsmooth, this minimization problem cannot be solved efficiently by an ordinary optimization method. We propose a decomposition scheme and an alternating optimization that can turn the problem into a set of minimizations of piecewise quadratic and univariate subproblems, each of which is a single variable vector problem, of either one dictionary atom or one coefficient vector. Although the subproblems are still nonsmooth, remarkably they become much simpler so that we can find a closed-form solution by introducing a proximal operator. This leads to an efficient algorithm for sparse representation. To our knowledge, applying the proximal operator to the problem with an incoherence term and obtaining the optimal dictionary atoms in closed form with a proximal operator technique have not previously been studied. The main advantages of the proposed algorithm are that, as suggested by our analysis and simulation study, it has lower computational complexity and a higher convergence rate than state-of-the-art algorithms. In addition, for real applications, it shows good performance and significant reductions in computational time.

  16. 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.

  17. Active Control of Automotive Intake Noise under Rapid Acceleration using the Co-FXLMS Algorithm

    NASA Astrophysics Data System (ADS)

    Lee, Hae-Jin; Lee, Gyeong-Tae; Oh, Jae-Eung

    The method of reducing automotive intake noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequency range (below 500 Hz) and is limited by the space of the engine room. However, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases significantly when the FXLMS algorithm is applied to the active control of intake noise under rapidly accelerating driving conditions. Therefore, in this study, the Co-FXLMS algorithm was proposed to improve the control performance of the FXLMS algorithm during rapid acceleration. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. The performance of the Co-FXLMS algorithm is presented in comparison with that of the FXLMS algorithm. Experimental results show that active noise control using Co-FXLMS is effective in reducing automotive intake noise during rapid acceleration.

  18. 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

  19. 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.

  20. 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…

  1. 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…

  2. 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…

  3. 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…

  4. Active Learning in the Library Instruction Environment: An Exploratory Study

    ERIC Educational Resources Information Center

    Ross, Alanna; Furno, Christine

    2011-01-01

    This paper describes an exploratory study investigating the impact of problem-based learning and clicker technology as active learning strategies at the American University of Sharjah Library, United Arab Emirates (UAE). Studies compared traditional and active learning classes. The present article maps the successes and challenges of these unique…

  5. 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…

  6. Navigated Active Learning in an International Academic Virtual Enterprise

    ERIC Educational Resources Information Center

    Horvath, Imre; Wiersma, Meindert; Duhovnik, Joze; Stroud, Ian

    2004-01-01

    Active learning is an educational paradigm that has been reinvented and methodologically underpinned many times in order to intensify learning in various forms. This paper presents a complex approach to active learning in a design-centred academic course with international participation. Research and design were considered as vehicles of active…

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

    PubMed

    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. PMID:26931852

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

    PubMed

    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.

  9. A Genetic Algorithm for Learning Significant Phrase Patterns in Radiology Reports

    SciTech Connect

    Patton, Robert M; Potok, Thomas E; Beckerman, Barbara G; Treadwell, Jim N

    2009-01-01

    Radiologists disagree with each other over the characteristics and features of what constitutes a normal mammogram and the terminology to use in the associated radiology report. Recently, the focus has been on classifying abnormal or suspicious reports, but even this process needs further layers of clustering and gradation, so that individual lesions can be more effectively classified. Using a genetic algorithm, the approach described here successfully learns phrase patterns for two distinct classes of radiology reports (normal and abnormal). These patterns can then be used as a basis for automatically analyzing, categorizing, clustering, or retrieving relevant radiology reports for the user.

  10. 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.

  11. Online adaptive policy learning algorithm for H∞ state feedback control of unknown affine nonlinear discrete-time systems.

    PubMed

    Zhang, Huaguang; Qin, Chunbin; Jiang, Bin; Luo, Yanhong

    2014-12-01

    The problem of H∞ state feedback control of affine nonlinear discrete-time systems with unknown dynamics is investigated in this paper. An online adaptive policy learning algorithm (APLA) based on adaptive dynamic programming (ADP) is proposed for learning in real-time the solution to the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in the H∞ control problem. In the proposed algorithm, three neural networks (NNs) are utilized to find suitable approximations of the optimal value function and the saddle point feedback control and disturbance policies. Novel weight updating laws are given to tune the critic, actor, and disturbance NNs simultaneously by using data generated in real-time along the system trajectories. Considering NN approximation errors, we provide the stability analysis of the proposed algorithm with Lyapunov approach. Moreover, the need of the system input dynamics for the proposed algorithm is relaxed by using a NN identification scheme. Finally, simulation examples show the effectiveness of the proposed algorithm. PMID:25095274

  12. 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.

  13. Bipart: Learning Block Structure for Activity Detection

    PubMed Central

    Mu, Yang; Lo, Henry Z.; Ding, Wei; Amaral, Kevin; Crouter, Scott E.

    2014-01-01

    Physical activity consists complex behavior, typically structured in bouts which can consist of one continuous movement (e.g. exercise) or many sporadic movements (e.g. household chores). Each bout can be represented as a block of feature vectors corresponding to the same activity type. This paper introduces a general distance metric technique to use this block representation to first predict activity type, and then uses the predicted activity to estimate energy expenditure within a novel framework. This distance metric, dubbed Bipart, learns block-level information from both training and test sets, combining both to form a projection space which materializes block-level constraints. Thus, Bipart provides a space which can improve the bout classification performance of all classifiers. We also propose an energy expenditure estimation framework which leverages activity classification in order to improve estimates. Comprehensive experiments on waist-mounted accelerometer data, comparing Bipart against many similar methods as well as other classifiers, demonstrate the superior activity recognition of Bipart, especially in low-information experimental settings. PMID:25328361

  14. Using Assistive Technology Adaptations To Include Students with Learning Disabilities in Cooperative Learning Activities.

    ERIC Educational Resources Information Center

    Bryant, Diane Pedrotty; Bryant, Brian R.

    1998-01-01

    Discusses a process for integrating technology adaptations for students with learning disabilities into cooperative-learning activities in terms of three components: (1) selecting adaptations; (2) monitoring use of adaptations during cooperative-learning activities; and (3) evaluating the adaptations' effectiveness. Barriers to and support systems…

  15. 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 study was…

  16. 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,…

  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

    2016-01-01

    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

  19. 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.

  20. 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.…

  1. "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. PMID:26330036

  2. Efficient Modeling and Active Learning Discovery of Biological Responses

    PubMed Central

    Naik, Armaghan W.; Kangas, Joshua D.; Langmead, Christopher J.; Murphy, Robert F.

    2013-01-01

    High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random. PMID:24358322

  3. How to Learn Effectively in Medical School: Test Yourself, Learn Actively, and Repeat in Intervals

    PubMed Central

    Augustin, Marc

    2014-01-01

    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. PMID:24910566

  4. 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

  5. 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.

  6. Automatic QSO Selection Algorithm Using Time Series Analysis and Machine Learning

    NASA Astrophysics Data System (ADS)

    Kim, Dae-Won; Protopapas, P.; Alcock, C.; Byun, Y.; Khardon, R.

    2011-01-01

    We present a new QSO selection algorithm using time series analysis and supervised machine learning. To characterize the lightcurves, we extracted multiple times series features such as period, amplitude, color and autocorrelation value. We then used Support Vector Machine (SVM), a supervised machine learning algorithm, to separate QSOs from other types of variable stars, microlensing events and non-variable stars. In order to train the QSO SVM model, we used 58 known QSOs, 1,629 variable stars and 4,288 non-variable stars from the MAssive Compact Halo Objects (MACHO) database. Cross-validation test shows that the model identifies 80% of known QSOs and have 25% false positive rate. Most of the false positives during the cross-validation are Be stars, known to show similar variability characteristic with QSOs. We applied the trained QSO SVM model to the MACHO Large Magellanic Cloud (LMC) dataset, which consists of 40million lightcurves, and found 1,097 QSO candidates. We crossmatched the candidates with several astronomical catalogs including the Spizter SAGE (Surveying the Agents of a Galaxy's Evolution) LMC catalog and various X-ray catalogs. The results suggest that the most of the candidates are likely true QSOs.

  7. Sensitivity of cloud retrieval statistics to algorithm choices: Lessons learned from MODIS product development

    NASA Astrophysics Data System (ADS)

    Platnick, Steven; Ackerman, Steven; King, Michael; Zhang, Zhibo; Wind, Galina

    2013-04-01

    Cloud detection algorithms search for measurement signatures that differentiate a cloud-contaminated or "not-clear" pixel from the clear-sky background. These signatures can be spectral, textural or temporal in nature. The magnitude of the difference between the cloud and the background must exceed a threshold value for the pixel to be classified having a not-clear FOV. All detection algorithms employ multiple tests ranging across some portion of the solar reflectance and/or infrared spectrum. However, a cloud is not a single, uniform object, but rather has a distribution of optical thickness and morphology. As a result, problems can arise when the distributions of cloud and clear-sky background characteristics overlap, making some test results indeterminate and/or leading to some amount of detection misclassification. Further, imager cloud retrieval statistics are highly sensitive to how a pixel identified as not-clear by a cloud mask is determined to be useful for cloud-top and optical retrievals based on 1-D radiative models. This presentation provides an overview of the different 'choices' algorithm developers make in cloud detection algorithms and the impact on regional and global cloud amounts and fractional coverage, cloud type and property distributions. Lessons learned over the course of the MODIS cloud product development history are discussed. As an example, we will focus on the 1km MODIS Collection 5 cloud optical retrieval algorithm (product MOD06/MYD06 for Terra and Aqua, respectively) which removed pixels associated with cloud edges as defined by immediate adjacency to clear FOV MODIS cloud mask (MOD35/MYD35) pixels as well as ocean pixels with partly cloudy elements in the 250m MODIS cloud mask - part of the so-called Clear Sky Restoral algorithm. The Collection 6 algorithm attempts retrievals for these two types of partly cloudy pixel populations, but allows a user to isolate or filter out the populations. Retrieval sensitivities for these

  8. Empathy and feedback processing in active and observational learning.

    PubMed

    Rak, Natalia; Bellebaum, Christian; Thoma, Patrizia

    2013-12-01

    The feedback-related negativity (FRN) and the P300 have been related to the processing of one's own and other individuals' feedback during both active and observational learning. The aim of the present study was to elucidate the role of trait-empathic responding with regard to the modulation of the neural correlates of observational learning in particular. Thirty-four healthy participants completed an active and an observational learning task. On both tasks, the participants' aim was to maximize their monetary gain by choosing from two stimuli the one that showed the higher probability of reward. Participants gained insight into the stimulus-reward contingencies according to monetary feedback presented after they had made an active choice or by observing the choices of a virtual partner. Participants showed a general improvement in learning performance on both learning tasks. P200, FRN, and P300 amplitudes were larger during active, as compared with observational, learning. Furthermore, nonreward elicited a significantly more negative FRN than did reward in the active learning task, while only a trend was observed for observational learning. Distinct subcomponents of trait cognitive empathy were related to poorer performance and smaller P300 amplitudes for observational learning only. Taken together, both the learning performance and event-related potentials during observational learning are affected by different aspects of trait cognitive empathy, and certain types of observational learning may actually be disrupted by a higher tendency to understand and adopt other people's perspectives.

  9. 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.

  10. 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. PMID:25222730

  11. 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.

  12. DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins.

    PubMed

    Jamali, Ali Akbar; Ferdousi, Reza; Razzaghi, Saeed; Li, Jiuyong; Safdari, Reza; Ebrahimie, Esmaeil

    2016-05-01

    Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning methods to predict whether a protein is druggable as well as to opt for superior algorithm in this task. In addition, feature selection procedures were used to provide the best performance of each classifier according to the optimum number of features. When run on all features, Neural Network was the best classifier, with 89.98% accuracy, based on a k-fold cross-validation test. Among all the algorithms applied, the optimum number of most-relevant features was 130, according to the Support Vector Machine-Feature Selection (SVM-FS) algorithm. This study resulted in the discovery of new drug target which potentially can be employed in cell signaling pathways, gene expression, and signal transduction. The DrugMiner web tool was developed based on the findings of this study to provide researchers with the ability to predict druggable proteins. DrugMiner is freely available at www.DrugMiner.org.

  13. Nonlinear Learning of Linear Algebra: Active Learning through Journal Writing

    ERIC Educational Resources Information Center

    Hamdan, May

    2005-01-01

    Students find difficulty in learning linear algebra because of the abstraction and formalism associated with concepts such as vector space, linear independence, rank and invertible matrices. Learning the necessary procedures becomes insufficient, and imitating worked examples does not guarantee the maturity level necessary for understanding these…

  14. 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.

  15. 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.

  16. 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

  17. Learning Activities Packages, Earth Science and Life Science.

    ERIC Educational Resources Information Center

    Miller Junior High School, Marshalltown, IA.

    Thirteen "Learning Activities Packages" for junior high school students focus on earth science and life science. Individual packages can be used with some lecture and films. Each learning activity package lists behavioral objectives and concepts to be used. Lists of reading assignments and references, along with laboratory activities, are also…

  18. 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.

  19. Writing To Learn Activities in Writing across the Curriculum Classrooms.

    ERIC Educational Resources Information Center

    DeNight, Shawn

    Content area teachers interested in using writing to learn activities need to be well informed about the learning promoted by certain writing tasks before assigning them. Writing assignments should not be thoughtlessly and arbitrarily assigned with the expectation that learning, somewhat miraculously or mysteriously, will occur. Although writing…

  20. 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)

  1. 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)…

  2. 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…

  3. 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…

  4. 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.…

  5. 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)

  6. 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).

  7. 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.

  8. 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…

  9. Computationally efficient algorithm for high sampling-frequency operation of active noise control

    NASA Astrophysics Data System (ADS)

    Rout, Nirmal Kumar; Das, Debi Prasad; Panda, Ganapati

    2015-05-01

    In high sampling-frequency operation of active noise control (ANC) system the length of the secondary path estimate and the ANC filter are very long. This increases the computational complexity of the conventional filtered-x least mean square (FXLMS) algorithm. To reduce the computational complexity of long order ANC system using FXLMS algorithm, frequency domain block ANC algorithms have been proposed in past. These full block frequency domain ANC algorithms are associated with some disadvantages such as large block delay, quantization error due to computation of large size transforms and implementation difficulties in existing low-end DSP hardware. To overcome these shortcomings, the partitioned block ANC algorithm is newly proposed where the long length filters in ANC are divided into a number of equal partitions and suitably assembled to perform the FXLMS algorithm in the frequency domain. The complexity of this proposed frequency domain partitioned block FXLMS (FPBFXLMS) algorithm is quite reduced compared to the conventional FXLMS algorithm. It is further reduced by merging one fast Fourier transform (FFT)-inverse fast Fourier transform (IFFT) combination to derive the reduced structure FPBFXLMS (RFPBFXLMS) algorithm. Computational complexity analysis for different orders of filter and partition size are presented. Systematic computer simulations are carried out for both the proposed partitioned block ANC algorithms to show its accuracy compared to the time domain FXLMS algorithm.

  10. Guided filter and adaptive learning rate based non-uniformity correction algorithm for infrared focal plane array

    NASA Astrophysics Data System (ADS)

    Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian

    2016-05-01

    Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.

  11. Active Learning in a Non-Majors Biology Class: Lessons Learned

    ERIC Educational Resources Information Center

    McClanahan, Elaine B.; McClanahan, Lon L.

    2002-01-01

    This article describes how a traditional biology lecture course was transformed into an interactive class. A review the activities used, changes made to grading policy, and practical tips for integration of active learning in the classroom are provided. Analysis of student responses to course assessments indicated that active learning experiences…

  12. How Do Teachers Learn in the Workplace? An Examination of Teacher Learning Activities

    ERIC Educational Resources Information Center

    Meirink, Jacobiene A.; Meijer, Paulien C.; Verloop, Nico; Bergen, Theo C. M.

    2009-01-01

    In this study, two data collection instruments were used to examine how Dutch secondary school teachers learn in the workplace. Firstly, they completed a questionnaire on their preferences for learning activities on two occasions. Secondly, during the intermediate period, they reported learning experiences in digital logs. Results of both…

  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. 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…

  15. Designing for Inquiry-Based Learning with the Learning Activity Management System

    ERIC Educational Resources Information Center

    Levy, P.; Aiyegbayo, O.; Little, S.

    2009-01-01

    This paper explores the relationship between practitioners' pedagogical purposes, values and practices in designing for inquiry-based learning in higher education, and the affordances of the Learning Activity Management System (LAMS) as a tool for creating learning designs in this context. Using a qualitative research methodology, variation was…

  16. 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…

  17. 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…

  18. 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.

  19. Students' Learning Activities While Studying Biological Process Diagrams

    NASA Astrophysics Data System (ADS)

    Kragten, Marco; Admiraal, Wilfried; Rijlaarsdam, Gert

    2015-08-01

    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 data and eye-tracking data were collected as indications of students' learning activities. For the verbal data, we applied a fine-grained coding scheme to optimally describe students' learning activities. For the eye-tracking data, we used fixation time and transitions between areas of interest in the process diagrams as indices of learning activities. Various learning activities while studying process diagrams were found that distinguished between more and less successful students. Results showed that between-student variance in comprehension score was highly predicted by meaning making of the process arrows (80%) and fixation time in the main area (65%). Students employed successful learning activities consistently across learning tasks. Furthermore, compared to unsuccessful students, successful students used a more coherent approach of interrelated learning activities for comprehending process diagrams.

  20. 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

  1. A new backpropagation learning algorithm for layered neural networks with nondifferentiable units.

    PubMed

    Oohori, Takahumi; Naganuma, Hidenori; Watanabe, Kazuhisa

    2007-05-01

    We propose a digital version of the backpropagation algorithm (DBP) for three-layered neural networks with nondifferentiable binary units. This approach feeds teacher signals to both the middle and output layers, whereas with a simple perceptron, they are given only to the output layer. The additional teacher signals enable the DBP to update the coupling weights not only between the middle and output layers but also between the input and middle layers. A neural network based on DBP learning is fast and easy to implement in hardware. Simulation results for several linearly nonseparable problems such as XOR demonstrate that the DBP performs favorably when compared to the conventional approaches. Furthermore, in large-scale networks, simulation results indicate that the DBP provides high performance. PMID:17381272

  2. A new backpropagation learning algorithm for layered neural networks with nondifferentiable units.

    PubMed

    Oohori, Takahumi; Naganuma, Hidenori; Watanabe, Kazuhisa

    2007-05-01

    We propose a digital version of the backpropagation algorithm (DBP) for three-layered neural networks with nondifferentiable binary units. This approach feeds teacher signals to both the middle and output layers, whereas with a simple perceptron, they are given only to the output layer. The additional teacher signals enable the DBP to update the coupling weights not only between the middle and output layers but also between the input and middle layers. A neural network based on DBP learning is fast and easy to implement in hardware. Simulation results for several linearly nonseparable problems such as XOR demonstrate that the DBP performs favorably when compared to the conventional approaches. Furthermore, in large-scale networks, simulation results indicate that the DBP provides high performance.

  3. 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.

  4. 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.

  5. 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…

  6. 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…

  7. 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…

  8. 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…

  9. The Learning Activities Questionnaire: A Tool to Enhance Teaching

    ERIC Educational Resources Information Center

    Ager, Richard

    2012-01-01

    This article describes the Learning Activities Questionnaire (LAQ) and how it can be employed to evaluate learning tasks not typically examined in course evaluation instruments such as readings and assignments. Drawing from behavioral theory in its focus on specific activities, this instrument is simple to interpret and provides clear direction…

  10. 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…

  11. 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…

  12. Photography. 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…

  13. 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…

  14. 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…

  15. 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…

  16. The Green Revolution in Transportation. Resource Recovery. Technology Learning Activities.

    ERIC Educational Resources Information Center

    Technology Teacher, 1991

    1991-01-01

    These two learning activities provide context, objectives, list of materials, student activity, and evaluation criteria. The first involves an automotive class in developing a model alternative fueled vehicle, and the second involves the design of a useful recyclable product. (JOW)

  17. A Typology of Learning Activities for International Business Education.

    ERIC Educational Resources Information Center

    Schertzer, Clinton B.; And Others

    A typology of learning activities for business education was developed at Xavier University (Cincinnati, Ohio) based on three primary goals for internationalization of business education: awareness, understanding, and competency. Fifteen types of internationalization pedagogical activities are identified: international examples, international…

  18. Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

    PubMed

    Stoica, C; Camejo, J; Banciu, A; Nita-Lazar, M; Paun, I; Cristofor, S; Pacheco, O R; Guevara, M

    2016-01-01

    Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

  19. Clustering WHO-ART terms using semantic distance and machine learning algorithms.

    PubMed

    Iavindrasana, Jimison; Bousquet, Cedric; Degoulet, Patrice; Jaulent, Marie-Christine

    2006-01-01

    WHO-ART was developed by the WHO collaborating centre for international drug monitoring in order to code adverse drug reactions. We assume that computation of semantic distance between WHO-ART terms may be an efficient way to group related medical conditions in the WHO database in order to improve signal detection. Our objective was to develop a method for clustering WHO-ART terms according to some proximity of their meanings. Our material comprises 758 WHO-ART terms. A formal definition was acquired for each term as a list of elementary concepts belonging to SNOMED international axes and characterized by modifier terms in some cases. Clustering was implemented as a terminology service on a J2EE server. Two different unsupervised machine learning algorithms (KMeans, Pvclust) clustered WHO-ART terms according to a semantic distance operator previously described. Pvclust grouped 51% of WHO-ART terms. K-Means grouped 100% of WHO-ART terms but 25% clusters were heterogeneous with k = 180 clusters and 6% clusters were heterogeneous with k = 32 clusters. Clustering algorithms associated to semantic distance could suggest potential groupings of WHO-ART terms that need validation according to the user's requirements.

  20. 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

  1. Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

    PubMed

    Stoica, C; Camejo, J; Banciu, A; Nita-Lazar, M; Paun, I; Cristofor, S; Pacheco, O R; Guevara, M

    2016-01-01

    Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria. PMID:27191562

  2. Postnatal TLR2 activation impairs learning and memory in adulthood.

    PubMed

    Madar, Ravit; Rotter, Aviva; Waldman Ben-Asher, Hiba; Mughal, Mohamed R; Arumugam, Thiruma V; Wood, W H; Becker, K G; Mattson, Mark P; Okun, Eitan

    2015-08-01

    Neuroinflammation in the central nervous system is detrimental for learning and memory, as evident form epidemiological studies linking developmental defects and maternal exposure to harmful pathogens. Postnatal infections can also induce neuroinflammatory responses with long-term consequences. These inflammatory responses can lead to motor deficits and/or behavioral disabilities. Toll like receptors (TLRs) are a family of innate immune receptors best known as sensors of microbial-associated molecular patterns, and are the first responders to infection. TLR2 forms heterodimers with either TLR1 or TLR6, is activated in response to gram-positive bacterial infections, and is expressed in the brain during embryonic development. We hypothesized that early postnatal TLR2-mediated neuroinflammation would adversely affect cognitive behavior in the adult. Our data indicate that postnatal TLR2 activation affects learning and memory in adult mice in a heterodimer-dependent manner. TLR2/6 activation improved motor function and fear learning, while TLR2/1 activation impaired spatial learning and enhanced fear learning. Moreover, developmental TLR2 deficiency significantly impairs spatial learning and enhances fear learning, stressing the involvement of the TLR2 pathway in learning and memory. Analysis of the transcriptional effects of TLR2 activation reveals both common and unique transcriptional programs following heterodimer-specific TLR2 activation. These results imply that adult cognitive behavior could be influenced in part, by activation or alterations in the TLR2 pathway at birth. PMID:26021559

  3. Postnatal TLR2 activation impairs learning and memory in adulthood

    PubMed Central

    Madar, Ravit; Rotter, Aviva; Ben-Asher, Hiba Waldman; Mughal, Mohamed R.; Arumugam, Thiruma V.; Wood, WH; Becker, KG; Mattson, Mark P.; Okun, Eitan

    2015-01-01

    Neuroinflammation in the central nervous system is detrimental for learning and memory, as evident form epidemiological studies linking developmental defects and maternal exposure to harmful pathogens. Postnatal infections can also induce neuroinflammatory responses with long-term consequences. These inflammatory responses can lead to motor deficits and/or behavioral disabilities. Toll like receptors (TLRs) are a family of innate immune receptors best known as sensors of microbial-associated molecular patterns, and are the first responders to infection. TLR2 forms heterodimers with either TLR1 or TLR6, is activated in response to gram-positive bacterial infections, and is expressed in the brain during embryonic development. We hypothesized that early postnatal TLR2-mediated neuroinflammation would adversely affect cognitive behavior in the adult. Our data indicate that postnatal TLR2 activation affects learning and memory in adult mice in a heterodimer-dependent manner. TLR2/6 activation improved motor function and fear learning, while TLR2/1 activation impaired spatial learning and enhanced fear learning. Moreover, developmental TLR2 deficiency significantly impairs spatial learning and enhances fear learning, stressing the involvement of the TLR2 pathway in learning and memory. Analysis of the transcriptional effects of TLR2 activation reveals both common and unique transcriptional programs following heterodimer-specific TLR2 activation. These results imply that adult cognitive behavior could be influenced in part, by activation or alterations in the TLR2 pathway at birth. PMID:26021559

  4. Postnatal TLR2 activation impairs learning and memory in adulthood.

    PubMed

    Madar, Ravit; Rotter, Aviva; Waldman Ben-Asher, Hiba; Mughal, Mohamed R; Arumugam, Thiruma V; Wood, W H; Becker, K G; Mattson, Mark P; Okun, Eitan

    2015-08-01

    Neuroinflammation in the central nervous system is detrimental for learning and memory, as evident form epidemiological studies linking developmental defects and maternal exposure to harmful pathogens. Postnatal infections can also induce neuroinflammatory responses with long-term consequences. These inflammatory responses can lead to motor deficits and/or behavioral disabilities. Toll like receptors (TLRs) are a family of innate immune receptors best known as sensors of microbial-associated molecular patterns, and are the first responders to infection. TLR2 forms heterodimers with either TLR1 or TLR6, is activated in response to gram-positive bacterial infections, and is expressed in the brain during embryonic development. We hypothesized that early postnatal TLR2-mediated neuroinflammation would adversely affect cognitive behavior in the adult. Our data indicate that postnatal TLR2 activation affects learning and memory in adult mice in a heterodimer-dependent manner. TLR2/6 activation improved motor function and fear learning, while TLR2/1 activation impaired spatial learning and enhanced fear learning. Moreover, developmental TLR2 deficiency significantly impairs spatial learning and enhances fear learning, stressing the involvement of the TLR2 pathway in learning and memory. Analysis of the transcriptional effects of TLR2 activation reveals both common and unique transcriptional programs following heterodimer-specific TLR2 activation. These results imply that adult cognitive behavior could be influenced in part, by activation or alterations in the TLR2 pathway at birth.

  5. 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.

  6. Searching for closely related ligands with different mechanisms of action using machine learning and mapping algorithms.

    PubMed

    Balfer, Jenny; Vogt, Martin; Bajorath, Jürgen

    2013-09-23

    Supervised machine learning approaches, including support vector machines, random forests, Bayesian classifiers, nearest-neighbor similarity searching, and a conceptually distinct mapping algorithm termed DynaMAD, have been investigated for their ability to detect structurally related ligands of a given receptor with different mechanisms of action. For this purpose, a large number of simulated virtual screening trials were carried out with models trained on mechanistic subsets of different classes of receptor ligands. The results revealed that ligands with the desired mechanism of action were frequently contained in database selection sets of limited size. All machine learning approaches successfully detected mechanistic subsets of ligands in a large background database of druglike compounds. However, the early enrichment characteristics considerably differed. Overall, random forests of relatively simple design and support vector machines with Gaussian kernels (Gaussian SVMs) displayed the highest search performance. In addition, DynaMAD was found to yield very small selection sets comprising only ~10 compounds that also contained ligands with the desired mechanism of action. Random forest, Gaussian SVM, and DynaMAD calculations revealed an enrichment of compounds with the desired mechanism over other mechanistic subsets. PMID:23952618

  7. 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…

  8. 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…

  9. 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

    2015-01-01

    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

  10. 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

  11. 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.

  12. Active Inference and Learning in the Cerebellum.

    PubMed

    Friston, Karl; Herreros, Ivan

    2016-09-01

    This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.

  13. Active Inference and Learning in the Cerebellum.

    PubMed

    Friston, Karl; Herreros, Ivan

    2016-09-01

    This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception. PMID:27391681

  14. 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. PMID:15740837

  15. 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…

  16. 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.

  17. Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications

    PubMed Central

    2014-01-01

    Background The huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U-BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n3) and of O(n5) order, respectively, and so, the algorithm is unaffordable for huge data sets. Results We find mathematical and programming solutions able to lead us towards the implementation of the algorithm U-BRAIN on parallel computers. First we give a Dynamic Programming model of the U-BRAIN algorithm, then we minimize the representation of the relevances. When the data are of great size we are forced to use the mass memory, and depending on where the data are actually stored, the access times can be quite different. According to the evaluation of algorithmic efficiency based on the Disk Model, in order to

  18. 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…

  19. 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…

  20. Attitudes of Face-to-Face and E-Learning Instructors toward "Active Learning"

    ERIC Educational Resources Information Center

    Pundak, David; Herscovitz, Orit; Shacham, Miri

    2010-01-01

    Instruction in higher education has developed significantly over the past two decades, influenced by two trends: promotion of active learning methods and integration of web technology in e-Learning. Many studies found that active teaching improves students' success, involvement and thinking skills. Nevertheless, internationally, most instructors…

  1. An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

    PubMed

    Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

    2013-12-01

    The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. PMID:24001924

  2. 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. PMID:24595155

  3. 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

  4. An Activity Theory View on Learning Studies

    ERIC Educational Resources Information Center

    Mosvold, Reidar; Bjuland, Raymond

    2011-01-01

    Learning study has been used by many to develop exemplary teaching in school, and this approach has recently been adopted for use in kindergarten as well. When using such approaches in different settings than they were intended for, several challenges potentially arise. This article discusses the implementation of a learning study approach in a…

  5. 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…

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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/.

  13. 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.

  14. Effects of activity and energy budget balancing algorithm on laboratory performance of a fish bioenergetics model

    USGS Publications Warehouse

    Madenjian, Charles P.; David, Solomon R.; Pothoven, Steven A.

    2012-01-01

    We evaluated the performance of the Wisconsin bioenergetics model for lake trout Salvelinus namaycush that were fed ad libitum in laboratory tanks under regimes of low activity and high activity. In addition, we compared model performance under two different model algorithms: (1) balancing the lake trout energy budget on day t based on lake trout energy density on day t and (2) balancing the lake trout energy budget on day t based on lake trout energy density on day t + 1. Results indicated that the model significantly underestimated consumption for both inactive and active lake trout when algorithm 1 was used and that the degree of underestimation was similar for the two activity levels. In contrast, model performance substantially improved when using algorithm 2, as no detectable bias was found in model predictions of consumption for inactive fish and only a slight degree of overestimation was detected for active fish. The energy budget was accurately balanced by using algorithm 2 but not by using algorithm 1. Based on the results of this study, we recommend the use of algorithm 2 to estimate food consumption by fish in the field. Our study results highlight the importance of accurately accounting for changes in fish energy density when balancing the energy budget; furthermore, these results have implications for the science of evaluating fish bioenergetics model performance and for more accurate estimation of food consumption by fish in the field when fish energy density undergoes relatively rapid changes.

  15. 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

  16. Activity Book. Catch the Spirit of Learning's Cooperation Games.

    ERIC Educational Resources Information Center

    Bernagozzi, Tom; And Others

    1992-01-01

    This activity book includes across-the-curriculum activities with Olympic themes; a "cooperation relay" (four competitive team activities based on a cooperative learning model); highlights of African Americans' Olympic achievements; a poster on teamwork and activities based on the theme of keeping the Olympic torch alive; and a reproducible…

  17. Motor skill learning requires active central myelination.

    PubMed

    McKenzie, Ian A; Ohayon, David; Li, Huiliang; de Faria, Joana Paes; Emery, Ben; Tohyama, Koujiro; Richardson, William D

    2014-10-17

    Myelin-forming oligodendrocytes (OLs) are formed continuously in the healthy adult brain. In this work, we study the function of these late-forming cells and the myelin they produce. Learning a new motor skill (such as juggling) alters the structure of the brain's white matter, which contains many OLs, suggesting that late-born OLs might contribute to motor learning. Consistent with this idea, we show that production of newly formed OLs is briefly accelerated in mice that learn a new skill (running on a "complex wheel" with irregularly spaced rungs). By genetically manipulating the transcription factor myelin regulatory factor in OL precursors, we blocked production of new OLs during adulthood without affecting preexisting OLs or myelin. This prevented the mice from mastering the complex wheel. Thus, generation of new OLs and myelin is important for learning motor skills. PMID:25324381

  18. 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.

  19. Algorithms in Learning, Teaching, and Instructional Design. Studies in Systematic Instruction and Training Technical Report 51201.

    ERIC Educational Resources Information Center

    Gerlach, Vernon S.; And Others

    An algorithm is defined here as an unambiguous procedure which will always produce the correct result when applied to any problem of a given class of problems. This paper gives an extended discussion of the definition of an algorithm. It also explores in detail the elements of an algorithm, the representation of algorithms in standard prose, flow…

  20. How to measure metallicity from five-band photometry with supervised machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Acquaviva, Viviana

    2016-02-01

    We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3, respectively. We find that ensemble methods, such as random forests of trees and extremely randomized trees and support vector machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which |Ztrue - Zpred| > 0.2 dex) is 2.2 and 3.9 per cent, respectively and the RMSE decreases to 0.068 and 0.069 if those objects are excluded. Because of the ability of these algorithms to capture complex relationships between data and target, our technique performs better than previously proposed methods that sought to fit metallicity using an analytic fitting formula, and has 3× more constraining power than SED fitting-based methods. Additionally, this method is extremely forgiving of contamination in the training set, and can be used with very satisfactory results for sample sizes of a few hundred objects. We distribute all the routines to reproduce our results and apply them to other data sets.

  1. Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data

    NASA Technical Reports Server (NTRS)

    Biswas, Rahul; Blackburn, Lindy L.; Cao, Junwei; Essick, Reed; Hodge, Kari Alison; Katsavounidis, Erotokritos; Kim, Kyungmin; Young-Min, Kim; Le Bigot, Eric-Olivier; Lee, Chang-Hwan; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.; Vaulin, Ruslan; Wang, Xiaoge; Ye, Tao

    2014-01-01

    The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitationalwave 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. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused 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. Terrestrial noise sources may manifest characteristic disturbances in these auxiliary channels, inducing non-trivial correlations with glitches in the gravitational-wave data. 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 very 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 very 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 limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract

  2. 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.

  3. 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…

  4. 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…

  5. Learning Through Movement: Teaching Cognitive Content through Physical Activities.

    ERIC Educational Resources Information Center

    Werner, Peter H.; Burton, Elsie C.

    Action-oriented learning activities are focused on in this book which attempts to outline an approach for stimulating and motivating children to learn through movement. The book is divided into five parts, each dealing with an aspect of the elementary school curriculum. Part one is concerned with the language arts and is divided into three…

  6. 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…

  7. Using Guided, Corpus-Aided Discovery to Generate Active Learning

    ERIC Educational Resources Information Center

    Huang, Li-Shih

    2008-01-01

    Over the years, educators have proposed a variety of active learning pedagogical approaches that focus on encouraging students to discover for themselves the principles and solutions that will engage them in learning and enhance their educational outcomes. Among these approaches are problem-based, inquiry-based, experiential, and discovery…

  8. 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…

  9. 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.…

  10. 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…

  11. 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…

  12. 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…

  13. 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…

  14. 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…

  15. 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…

  16. 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".…

  17. 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.

  18. 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…

  19. 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…

  20. 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…

  1. 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.

  2. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.

    PubMed

    Munsell, Brent C; Wee, Chong-Yaw; Keller, Simon S; Weber, Bernd; Elger, Christian; da Silva, Laura Angelica Tomaz; Nesland, Travis; Styner, Martin; Shen, Dinggang; Bonilha, Leonardo

    2015-09-01

    The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with "expert-based" clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy.

  3. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

    PubMed Central

    Munsell, Brent C.; Wee, Chong-Yaw; Keller, Simon S.; Weber, Bernd; Elger, Christian; da Silva, Laura Angelica Tomaz; Nesland, Travis; Styner, Martin; Shen, Dinggang; Bonilha, Leonardo

    2015-01-01

    The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with “expert-based” clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy. PMID:26054876

  4. A stable learning algorithm for block-diagonal recurrent neural networks: application to the analysis of lung sounds.

    PubMed

    Mastorocostas, Paris A; Theocharis, John B

    2006-04-01

    A novel learning algorithm, the Recurrent Neural Network Constrained Optimization Method (RENNCOM) is suggested in this paper, for training block-diagonal recurrent neural networks. The training task is formulated as a constrained optimization problem, whose objective is twofold: (1) minimization of an error measure, leading to successful approximation of the input/output mapping and (2) optimization of an additional functional, the payoff function, which aims at ensuring network stability throughout the learning process. Having assured the network and training stability conditions, the payoff function is switched to an alternative form with the scope to accelerate learning. Simulation results on a benchmark identification problem demonstrate that, compared to other learning schemes with stabilizing attributes, the RENNCOM algorithm has enhanced qualities, including, improved speed of convergence, accuracy and robustness. The proposed algorithm is also applied to the problem of the analysis of lung sounds. Particularly, a filter based on block-diagonal recurrent neural networks is developed, trained with the RENNCOM method. Extensive experimental results are given and performance comparisons with a series of other models are conducted, underlining the effectiveness of the proposed filter.

  5. Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

    USGS Publications Warehouse

    Louis, S.J.; Raines, G.L.

    2003-01-01

    We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

  6. An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks.

    PubMed

    Penumalli, Chakradhar; Palanichamy, Yogesh

    2015-01-01

    A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results.

  7. Active-passive correlation spectroscopy - A new technique for identifying ocean color algorithm spectral regions

    NASA Technical Reports Server (NTRS)

    Hoge, F. E.; Swift, R. N.

    1986-01-01

    A new active-passive airborne data correlation technique has been developed which allows the validation of existing in-water oceoan color algorithms and the rapid search, identification, and evaluation of new sensor band locations and algorithm wavelength intervals. Thus far, applied only in conjunction with the spectral curvature algorithm (SCA), the active-passive correlation spectroscopy (APCS) technique shows that (1) the usual 490-nm (center-band) chlorophyll SCA could satisfactorily be placed anywhere within the nominal 460-510-nm interval, and (2) two other spectral regions, 645-660 and 680-695 nm, show considerable promise for chlorophyll pigment measurement. Additionally, the APCS method reveals potentially useful wavelength regions (at 600 and about 670 nm) of very low chlorophyll-in-water spectral curvature into which accessory pigment algorithms for phycoerythrin might be carefully positioned. In combination, the APCS and SCA methods strongly suggest that significant information content resides within the seemingly featureless ocean color spectrum.

  8. Effect of Learning Activity on Students' Motivation, Physical Activity Levels and Effort/Persistence

    ERIC Educational Resources Information Center

    Gao, Zan; Lee, Amelia M.; Xiang, Ping; Kosma, Maria

    2011-01-01

    The type of learning activity offered in physical education may influence students' motivational beliefs, physical activity participation and effort/persistence in class. However, most empirical studies have focused on the individual level rather than on the learner-content interactions. Accordingly, the potential effects of learning activities on…

  9. Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks.

    PubMed

    Peña, J M; Lozano, J A; Larrañaga, P

    2005-01-01

    Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

  10. Physical Activity and Wellness: Applied Learning through Collaboration

    ERIC Educational Resources Information Center

    Long, Lynn Hunt; Franzidis, Alexia

    2015-01-01

    This article describes how two university professors teamed up to initiate a university-sponsored physical activity and wellness expo in an effort to promote an authentic and transformative learning experience for preservice students.

  11. Development through Dissent: Campus Activism as Civic Learning

    ERIC Educational Resources Information Center

    Biddix, J. Patrick

    2014-01-01

    This chapter traces two decades of published research on learning outcomes related to campus activism and reports results from a speculative study considering civic outcomes from participation in campus political and war demonstrations.

  12. Learning Activities: Students and Recycling. [and] Automobile Aerodynamics.

    ERIC Educational Resources Information Center

    McLaughlin, Charles H., Jr.; Schieber, Rich

    1994-01-01

    The first learning activity is intended to heighten students' awareness of the need for recycling, reuse, and reduction of materials; the second explores the aerodynamics of automobiles. Both include context, concept, objectives, procedure, and materials needed. (SK)

  13. ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction

    PubMed Central

    Wu, Sitao; Zhang, Yang

    2008-01-01

    We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ∼10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0×10−300 (or <1.0×10−148) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10–20°) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling. PMID:18923703

  14. Teaching Middle School Students with Learning Disabilities To Recruit Peer Assistance during Cooperative Learning Group Activities.

    ERIC Educational Resources Information Center

    Wolford, Patricia L.; Heward, William L.; Alber, Sheila R.

    2001-01-01

    Four 8th graders with learning disabilities were taught to recruit assistance from peers during cooperative learning activities in two general classrooms. Training consisted of modeling, role playing, corrective feedback, and praise. Recruitment training increased the productivity and accuracy with which the students completed their language arts…

  15. Using the Learning Activities Survey to Examine Transformative Learning Experiences in Two Graduate Teacher Preparation Courses

    ERIC Educational Resources Information Center

    Caruana, Vicki; Woodrow, Kelli; Pérez, Luis

    2015-01-01

    The Learning Activities Survey (LAS) detected whether, and to what extent, a perspective transformation occurred during two graduate courses in teacher preparation. The LAS examined the types of learning identified as contributing to their transformative experiences. This study examined pre-service teachers' critical reflection of the course…

  16. Student's Reflections on Their Learning and Note-Taking Activities in a Blended Learning Course

    ERIC Educational Resources Information Center

    Nakayama, Minoru; Mutsuura, Kouichi; Yamamoto, Hiroh

    2016-01-01

    Student's emotional aspects are often discussed in order to promote better learning activity in blended learning courses. To observe these factors, course participant's self-efficacy and reflections upon their studies were surveyed, in addition to the surveying of the metrics of student's characteristics during a Bachelor level credit course.…

  17. Learning Active Citizenship: Conflicts between Students' Conceptualisations of Citizenship and Classroom Learning Experiences in Lebanon

    ERIC Educational Resources Information Center

    Akar, Bassel

    2016-01-01

    Education for active citizenship continues to be a critical response for social cohesion and reconstruction in conflict-affected areas. Oftentimes, approaches to learning and teaching in such contexts can do as much harm as good. This study qualitatively examines 435 students' reflections of their civics classroom learning experiences and their…

  18. OpenSim-Supported Virtual Learning Environment: Transformative Content Representation, Facilitation, and Learning Activities

    ERIC Educational Resources Information Center

    Kim, Heesung; Ke, Fengfeng

    2016-01-01

    The pedagogical and design considerations for the use of a virtual reality (VR) learning environment are important for prospective and current teachers. However, empirical research investigating how preservice teachers interact with transformative content representation, facilitation, and learning activities in a VR educational simulation is still…

  19. Social Studies Activity Learning Center. A New Dimension in Personalized Learning.

    ERIC Educational Resources Information Center

    Culley, Roy J.

    At Marshfield Senior High School, over one-fourth of the students were note profiting from the required social studies course, as evidenced by grades and attendance. As a result, a study was made to improve social study learning experiences in order to conform to eight assumptions about student needs, such as students need activity learning;…

  20. Resting alpha activity predicts learning ability in alpha neurofeedback

    PubMed Central

    Wan, Feng; Nan, Wenya; Vai, Mang I.; Rosa, Agostinho

    2014-01-01

    Individuals differ in their ability to learn how to regulate the brain activity by neurofeedback. This study aimed to investigate whether the resting alpha activity can predict the learning ability in alpha neurofeedback. A total of 25 subjects performed 20 sessions of individualized alpha neurofeedback and the learning ability was assessed by three indices respectively: the training parameter changes between two periods, within a short period and across the whole training time. It was found that the resting alpha amplitude measured before training had significant positive correlations with all learning indices and could be used as a predictor for the learning ability prediction. This finding would help the researchers in not only predicting the training efficacy in individuals but also gaining further insight into the mechanisms of alpha neurofeedback. PMID:25071528