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…
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
An Active Learning Algorithm for Control of Epidural Electrostimulation.
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. PMID:25974925
Gamma-ray active galactic nucleus type through machine-learning algorithms
NASA Astrophysics Data System (ADS)
Hassan, T.; Mirabal, N.; Contreras, J. L.; Oya, I.
2013-01-01
The Fermi Gamma-ray Space Telescope (Fermi) is producing the most detailed inventory of the gamma-ray sky to date. Despite tremendous achievements approximately 25 per cent of all Fermi extragalactic sources in the Second Fermi Large Area Telescope Catalogue (2FGL) are listed as active galactic nuclei (AGN) of uncertain type. Typically, these are suspected blazar candidates without a conclusive optical spectrum or lacking spectroscopic observations. Here, we explore the use of machine-learning algorithms - random forests and support vector machines - to predict specific AGN subclass based on observed gamma-ray spectral properties. After training and testing on identified/associated AGN from the 2FGL we find that 235 out of 269 AGN of uncertain type have properties compatible with gamma-ray BL Lacertae and flat-spectrum radio quasars with accuracy rates of 85 per cent. Additionally, direct comparison of our results with class predictions made after following the infrared colour-colour space of Massaro et al. shows that the agreement rate is over four-fifths for 54 overlapping sources, providing independent cross-validation. These results can help tailor follow-up spectroscopic programmes and inform future pointed surveys with ground-based Cherenkov telescopes.
Cascade Error Projection Learning Algorithm
NASA Technical Reports Server (NTRS)
Duong, T. A.; Stubberud, A. R.; Daud, T.
1995-01-01
A detailed mathematical analysis is presented 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.
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.
Constructive neural network learning algorithms
Parekh, R.; Yang, Jihoon; Honavar, V.
1996-12-31
Constructive Algorithms offer an approach for incremental construction of potentially minimal neural network architectures for pattern classification tasks. These algorithms obviate the need for an ad-hoc a-priori choice of the network topology. The constructive algorithm design involves alternately augmenting the existing network topology by adding one or more threshold logic units and training the newly added threshold neuron(s) using a stable variant of the perception learning algorithm (e.g., pocket algorithm, thermal perception, and barycentric correction procedure). Several constructive algorithms including tower, pyramid, tiling, upstart, and perception cascade have been proposed for 2-category pattern classification. These algorithms differ in terms of their topological and connectivity constraints as well as the training strategies used for individual neurons.
Ensemble algorithms in reinforcement learning.
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
The Dropout Learning Algorithm
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
Cascade Error Projection: A New Learning Algorithm
NASA Technical Reports Server (NTRS)
Duong, T. A.; Stubberud, A. R.; Daud, T.; Thakoor, A. P.
1995-01-01
A new neural network architecture and a hardware implementable learning algorithm is proposed. The algorithm, called cascade error projection (CEP), handles lack of precision and circuit noise better than existing algorithms.
Natural gradient learning algorithms for RBF networks.
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
The annealing robust backpropagation (ARBP) learning algorithm.
Chuang, C C; Su, S F; Hsiao, C C
2000-01-01
Multilayer feedforward neural networks are often referred to as universal approximators. Nevertheless, if the used training data are corrupted by large noise, such as outliers, traditional backpropagation learning schemes may not always come up with acceptable performance. Even though various robust learning algorithms have been proposed in the literature, those approaches still suffer from the initialization problem. In those robust learning algorithms, the so-called M-estimator is employed. For the M-estimation type of learning algorithms, the loss function is used to play the role in discriminating against outliers from the majority by degrading the effects of those outliers in learning. However, the loss function used in those algorithms may not correctly discriminate against those outliers. In this paper, the annealing robust backpropagation learning algorithm (ARBP) that adopts the annealing concept into the robust learning algorithms is proposed to deal with the problem of modeling under the existence of outliers. The proposed algorithm has been employed in various examples. Those results all demonstrated the superiority over other robust learning algorithms independent of outliers. In the paper, not only is the annealing concept adopted into the robust learning algorithms but also the annealing schedule k/t was found experimentally to achieve the best performance among other annealing schedules, where k is a constant and is the epoch number. PMID:18249835
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…
Collet, Timothé; Pietquin, Olivier
2015-01-01
Active learning is the problem of interactively constructing the training set used in classification in order to reduce its size. It would ideally successively add the instance-label pair that decreases the classification error most. However, the effect of the addition of a pair is not known in advance. It can still be estimated with the pairs already in the training set. The online minimization of the classification error involves a tradeoff between exploration and exploitation. This is a common problem in machine learning for which multiarmed bandit, using the approach of Optimism int the Face of Uncertainty, has proven very efficient these last years. This paper introduces three algorithms for the active learning problem in classification using Optimism in the Face of Uncertainty. Experiments lead on built-in problems and real world datasets demonstrate that they compare positively to state-of-the-art methods. PMID:26681934
Generation of attributes for learning algorithms
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.
Parameter incremental learning algorithm for neural networks.
Wan, Sheng; Banta, Larry E
2006-11-01
In this paper, a novel stochastic (or online) training algorithm for neural networks, named parameter incremental learning (PIL) algorithm, is proposed and developed. The main idea of the PIL strategy is that the learning algorithm should not only adapt to the newly presented input-output training pattern by adjusting parameters, but also preserve the prior results. A general PIL algorithm for feedforward neural networks is accordingly presented as the first-order approximate solution to an optimization problem, where the performance index is the combination of proper measures of preservation and adaptation. The PIL algorithms for the multilayer perceptron (MLP) are subsequently derived. Numerical studies show that for all the three benchmark problems used in this paper the PIL algorithm for MLP is measurably superior to the standard online backpropagation (BP) algorithm and the stochastic diagonal Levenberg-Marquardt (SDLM) algorithm in terms of the convergence speed and accuracy. Other appealing features of the PIL algorithm are that it is computationally as simple as the BP algorithm, and as easy to use as the BP algorithm. It, therefore, can be applied, with better performance, to any situations where the standard online BP algorithm is applicable. PMID:17131658
Automating parallel implementation of neural learning algorithms.
Rana, O F
2000-06-01
Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries
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)
Clustering algorithms do not learn, but they can be learned
NASA Astrophysics Data System (ADS)
Brun, Marcel; Dougherty, Edward R.
2005-08-01
Pattern classification theory involves an error criterion, optimal classifiers, and a theory of learning. For clustering, there has historically been little theory; in particular, there has generally (but not always) been no learning. The key point is that clustering has not been grounded on a probabilistic theory. Recently, a clustering theory has been developed in the context of random sets. This paper discusses learning within that context, in particular, k- nearest-neighbor learning of clustering algorithms.
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.
Initiative learning algorithm based on rough set
NASA Astrophysics Data System (ADS)
Wang, Guoyin; He, Xiao
2003-03-01
Rough set theory is emerging as a new tool for dealing with fuzzy and uncertain data. In this paper, a theory is developed to express, measure and process uncertain information and uncertain knowledge based on our result about the uncertainty measure of decision tables and decision rule systems. Based on Skowron"s propositional default rule generation algorithm, we develop an initiative learning model with rough set based initiative rule generation algorithm. Simulation results illustrate its efficiency.
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.
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)
ERIC Educational Resources Information Center
Zayapragassarazan, Z.; Kumar, Santosh
2012-01-01
Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…
Paradigms for Realizing Machine Learning Algorithms.
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
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
TAO-robust backpropagation learning algorithm.
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. PMID:15795116
Dictionary Learning Algorithms for Sparse Representation
Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.
2010-01-01
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial “25 words or less”), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an over-complete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error). PMID:12590811
Learning algorithms for stack filter classifiers
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.
Cascade Error Projection: An Efficient Hardware Learning Algorithm
NASA Technical Reports Server (NTRS)
Duong, T. A.
1995-01-01
A new learning algorithm termed cascade error projection (CEP) is presented. CEP is an adaption of a constructive architecture from cascade correlation and the dynamical stepsize of A/D conversion from the cascade back propagation algorithm.
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
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.
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…
Phoneme recognition with kernel learning algorithms
NASA Astrophysics Data System (ADS)
Namarvar, Hassan H.; Berger, Theodore W.
2004-10-01
An isolated phoneme recognition system is proposed using time-frequency domain analysis and support vector machines (SVMs). The TIMIT corpus which contains a total of 6300 sentences, ten sentences spoken by each of 630 speakers from eight major dialect regions of the United States, was used in this experiment. Provided time-aligned phonetic transcription was used to extract phonemes from speech samples. A 55-output classifier system was designed corresponding to 55 classes of phonemes and trained with the kernel learning algorithms. The training dataset was extracted from clean training samples. A portion of the database, i.e., 65338 samples of training dataset, was used to train the system. The performance of the system on the training dataset was 76.4%. The whole test dataset of the TIMIT corpus was used to test the generalization of the system. All samples, i.e., 55655 samples of the test dataset, were used to test the system. The performance of the system on the test dataset was 45.3%. This approach is currently under development to extend the algorithm for continuous phoneme recognition. [Work supported in part by grants from DARPA, NASA, and ONR.
On-line learning algorithms for locally recurrent neural networks.
Campolucci, P; Uncini, A; Piazza, F; Rao, B D
1999-01-01
This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasis on multilayer perceptron (MLP) with infinite impulse response (IIR) synapses and its variations which include generalized output and activation feedback multilayer networks (MLN's). We propose a new gradient-based procedure called recursive backpropagation (RBP) whose on-line version, causal recursive backpropagation (CRBP), presents some advantages with respect to the other on-line training methods. The new CRBP algorithm includes as particular cases backpropagation (BP), temporal backpropagation (TBP), backpropagation for sequences (BPS), Back-Tsoi algorithm among others, thereby providing a unifying view on gradient calculation techniques for recurrent networks with local feedback. The only learning method that has been proposed for locally recurrent networks with no architectural restriction is the one by Back and Tsoi. The proposed algorithm has better stability and higher speed of convergence with respect to the Back-Tsoi algorithm, which is supported by the theoretical development and confirmed by simulations. The computational complexity of the CRBP is comparable with that of the Back-Tsoi algorithm, e.g., less that a factor of 1.5 for usual architectures and parameter settings. The superior performance of the new algorithm, however, easily justifies this small increase in computational burden. In addition, the general paradigms of truncated BPTT and RTRL are applied to networks with local feedback and compared with the new CRBP method. The simulations show that CRBP exhibits similar performances and the detailed analysis of complexity reveals that CRBP is much simpler and easier to implement, e.g., CRBP is local in space and in time while RTRL is not local in space. PMID:18252525
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…
Active Learning in the Era of Big Data
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.
Gradient descent learning algorithm overview: a general dynamical systems perspective.
Baldi, P
1995-01-01
Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning. PMID:18263297
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…
Automated training for algorithms that learn from genomic data.
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. PMID:25695053
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.
Active Learning: Learning a Motor Skill Without a Coach
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
Active learning: learning a motor skill without a coach.
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
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…
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
Creative Activity and Learning.
ERIC Educational Resources Information Center
Cunningham, Flora E.
1979-01-01
This article compares three theories of the creative process taken from aesthetic philosophy: aesthetic enjoyment (D. W. Gotshalk), aesthetic experience (John Dewey), and aesthetic knowledge (Susanne Langer). Each shows different versions of the learning that accrues from creative activity. From this, curriculum planning and teaching suggestions…
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.
On stochastic approximation algorithms for classes of PAC learning problems
Rao, N.S.V.; Uppuluri, V.R.R.; Oblow, E.M.
1994-03-01
The classical stochastic approximation methods are shown to yield algorithms to solve several formulations of the PAC learning problem defined on the domain [o,1]{sup d}. Under some assumptions on different ability of the probability measure functions, simple algorithms to solve some PAC learning problems are proposed based on networks of non-polynomial units (e.g. artificial neural networks). Conditions on the sizes of these samples required to ensure the error bounds are derived using martingale inequalities.
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…
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.
Protein sequence classification with improved extreme learning machine algorithms.
Cao, Jiuwen; Xiong, Lianglin
2014-01-01
Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms. PMID:24795876
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.
Evolving Stochastic Learning Algorithm based on Tsallis entropic index
NASA Astrophysics Data System (ADS)
Anastasiadis, A. D.; Magoulas, G. D.
2006-03-01
In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks. The new algorithm combines deterministic and stochastic search steps by employing a different adaptive stepsize for each network weight, and applies a form of noise that is characterized by the nonextensive entropic index q, regulated by a weight decay term. The behavior of the learning algorithm can be made more stochastic or deterministic depending on the trade off between the temperature T and the q values. This is achieved by introducing a formula that defines a time-dependent relationship between these two important learning parameters. Our experimental study verifies that there are indeed improvements in the convergence speed of this new evolving stochastic learning algorithm, which makes learning faster than using the original Hybrid Learning Scheme (HLS). In addition, experiments are conducted to explore the influence of the entropic index q and temperature T on the convergence speed and stability of the proposed method.
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.
Finite-sample based learning algorithms for feedforward networks
Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.
1995-04-01
We discuss two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by FeedForward Networks (FFN). The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can also be directly applied to concept learning problems. A main distinguishing feature of the this work is that the sample sizes are based on explicit algorithms rather than information-based methods.
A fast and convergent stochastic MLP learning algorithm.
Sakurai, A
2001-12-01
We propose a stochastic learning algorithm for multilayer perceptrons of linear-threshold function units, which theoretically converges with probability one and experimentally exhibits 100% convergence rate and remarkable speed on parity and classification problems with typical generalization accuracy. For learning the n bit parity function with n hidden units, the algorithm converged on all the trials we tested (n=2 to 12) after 5.8 x 4.1(n) presentations for 0.23 x 4.0(n-6) seconds on a 533MHz Alpha 21164A chip on average, which is five to ten times faster than Levenberg-Marquardt algorithm with restarts. For a medium size classification problem known as Thyroid in UCI repository, the algorithm is faster in speed and comparative in generalization accuracy than the standard backpropagation and Levenberg-Marquardt algorithms. PMID:11852440
Constructive neural-network learning algorithms for pattern classification.
Parekh, R; Yang, J; Honavar, V
2000-01-01
Constructive learning algorithms offer an attractive approach for the incremental construction of near-minimal neural-network architectures for pattern classification. They help overcome the need for ad hoc and often inappropriate choices of network topology in algorithms that search for suitable weights in a priori fixed network architectures. Several such algorithms are proposed in the literature and shown to converge to zero classification errors (under certain assumptions) on tasks that involve learning a binary to binary mapping (i.e., classification problems involving binary-valued input attributes and two output categories). We present two constructive learning algorithms MPyramid-real and MTiling-real that extend the pyramid and tiling algorithms, respectively, for learning real to M-ary mappings (i.e., classification problems involving real-valued input attributes and multiple output classes). We prove the convergence of these algorithms and empirically demonstrate their applicability to practical pattern classification problems. Additionally, we show how the incorporation of a local pruning step can eliminate several redundant neurons from MTiling-real networks. PMID:18249773
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.
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.
Active inference and learning.
Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O'Doherty, John; Pezzulo, Giovanni
2016-09-01
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. PMID:27375276
Learning algorithms for feedforward networks based on finite samples
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.
Learning algorithm of environmental recognition in driving vehicle
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.
Optimization of circuits using a constructive learning algorithm
Beiu, V.
1997-05-01
The paper presents an application of a constructive learning algorithm to optimization of circuits. For a given Boolean function f. a fresh constructive learning algorithm builds circuits belonging to the smallest F{sub n,m} class of functions (n inputs and having m groups of ones in their truth table). The constructive proofs, which show how arbitrary Boolean functions can be implemented by this algorithm, are shortly enumerated An interesting aspect is that the algorithm can be used for generating both classical Boolean circuits and threshold gate circuits (i.e. analogue inputs and digital outputs), or a mixture of them, thus taking advantage of mixed analogue/digital technologies. One illustrative example is detailed The size and the area of the different circuits are compared (special cost functions can be used to closer estimate the area and the delay of VLSI implementations). Conclusions and further directions of research are ending the paper.
Gradient Learning Algorithms for Ontology Computing
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
TS: a test-split algorithm for inductive learning
NASA Astrophysics Data System (ADS)
Wu, Xindong
1993-09-01
This paper presents a new attribute-based learning algorithm, TS. Different from ID3, AQ11, and HCV in strategies, this algorithm operates in cycles of test and split. It uses those attribute values which occur only in positives but not in negatives to straightforwardly discriminate positives against negatives and chooses the attributes with least number of different values to split example sets. TS is natural, easy to implement, and low-order polynomial in time complexity.
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…
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…
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.
Managing and learning with multiple models: Objectives and optimization algorithms
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.
Scalable histopathological image analysis via active learning.
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
Robot navigation algorithms using learned spatial graphs
Iyengar, S.S.; Jorgensen, C.C.; Rao, S.V.N.; Weisbin, C.R.
1985-01-01
Finding optimal paths for robot navigation in known terrain has been studied for some time but, in many important situations, a robot would be required to navigate in completely new or partially explored terrain. We propose a method of robot navigation which requires no pre-learned model, makes maximal use of available information, records and synthesizes information from multiple journeys, and contains concepts of learning that allow for continuous transition from local to global path optimality. The model of the terrain consists of a spatial graph and a Voronoi diagram. Using acquired sensor data, polygonal boundaries containing perceived obstacles shrink to approximate the actual obstacles' surfaces, free space for transit is correspondingly enlarged, and additional nodes and edges are recorded based on path intersections and stop points. Navigation planning is gradually accelerated with experience since improved global map information minimizes the need for further sensor data acquisition. Our method currently assumes obstacle locations are unchanging, navigation can be successfully conducted using two-dimensional projections, and sensor information is precise.
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.
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.
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)…
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…
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…
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…
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.
ERIC Educational Resources Information Center
Boulton-Lewis, Gillian M.; Buys, Laurie; Lovie-Kitchin, Jan
2006-01-01
Learning is an important aspect of aging productively. This paper describes results from 2645 respondents (aged from 50 to 74+ years) to a 165-variable postal survey in Australia. The focus is on learning and its relation to work; social, spiritual, and emotional status; health; vision; home; life events; and demographic details. Clustering…
Simple randomized algorithms for online learning with kernels.
He, Wenwu; Kwok, James T
2014-12-01
In online learning with kernels, it is vital to control the size (budget) of the support set because of the curse of kernelization. In this paper, we propose two simple and effective stochastic strategies for controlling the budget. Both algorithms have an expected regret that is sublinear in the horizon. Experimental results on a number of benchmark data sets demonstrate encouraging performance in terms of both efficacy and efficiency. PMID:25108150
Transfer Learning for Activity Recognition: A Survey
Cook, Diane; Feuz, Kyle D.; Krishnan, Narayanan C.
2013-01-01
Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. PMID:24039326
Recursive least-squares learning algorithms for neural networks
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.
Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.
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
The No-Prop algorithm: a new learning algorithm for multilayer neural networks.
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
Sparse kernel learning with LASSO and Bayesian inference algorithm.
Gao, Junbin; Kwan, Paul W; Shi, Daming
2010-03-01
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, P. H. (2008). L1 LASSO and its Bayesian inference. In W. Wobcke, & M. Zhang (Eds.), Lecture notes in computer science: Vol. 5360 (pp. 318-324); Wang, G., Yeung, D. Y., & Lochovsky, F. (2007). The kernel path in kernelized LASSO. In International conference on artificial intelligence and statistics (pp. 580-587). San Juan, Puerto Rico: MIT Press]. This paper is concerned with learning kernels under the LASSO formulation via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages. PMID:19604671
Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.
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
Improve online boosting algorithm from self-learning cascade classifier
NASA Astrophysics Data System (ADS)
Luo, Dapeng; Sang, Nong; Huang, Rui; Tong, Xiaojun
2010-04-01
Online boosting algorithm has been used in many vision-related applications, such as object detection. However, in order to obtain good detection result, combining a large number of weak classifiers into a strong classifier is required. And those weak classifiers must be updated and improved online. So the training and detection speed will be reduced inevitably. This paper proposes a novel online boosting based learning method, called self-learning cascade classifier. Cascade decision strategy is integrated with the online boosting procedure. The resulting system contains enough number of weak classifiers while keeping computation cost low. The cascade structure is learned and updated online. And the structure complexity can be increased adaptively when detection task is more difficult. Moreover, most of new samples are labeled by tracking automatically. This can greatly reduce the effort by labeler. We present experimental results that demonstrate the efficient and high detection rate of the method.
Inference algorithms and learning theory for Bayesian sparse factor analysis
NASA Astrophysics Data System (ADS)
Rattray, Magnus; Stegle, Oliver; Sharp, Kevin; Winn, John
2009-12-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
Ye, Cang; Yung, N C; Wang, Danwei
2003-01-01
Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system. PMID:18238153
Volume learning algorithm artificial neural networks for 3D QSAR studies.
Tetko, I V; Kovalishyn, V V; Livingstone, D J
2001-07-19
The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies. PMID:11448223
An active learning approach with uncertainty, representativeness, and diversity.
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
An Active Learning Approach with Uncertainty, Representativeness, and Diversity
He, Tianxu; Zhang, Shukui; Xin, Jie; 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
Artificial Bee Colony Algorithm Based on Information Learning.
Gao, Wei-Feng; Huang, Ling-Ling; Liu, San-Yang; Dai, Cai
2015-12-01
Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms. PMID:25594992
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…
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…
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.
Open Space Learning Activities
ERIC Educational Resources Information Center
Knapp, Clifford E.
1976-01-01
Describes a science activity in which students are given an opportunity to consider the values of open space. The program includes direct involvement as communicators of feelings and facts, leading students to a position of making wise decisions for land use in the future. (EB)
Analysis of Pollution Patterns Using Unsupervised Machine Learning Algorithms
NASA Astrophysics Data System (ADS)
Kanevski, M.; Timonin, V.; Pozdnoukhov, A.; Maignan, M.
2009-04-01
The research presents an application of Machine Learning Algorithms, mainly unsupervised learning techniques like self-organising Kohonen maps (SOM), to study spatial patterns of multivariate environmental spatial data. SOM are well-known neural networks widely used for high-dimensional data analysis, modelling (clustering and classification), and visualization. Self-organising maps belong to the unsupervised machine learning algorithms providing solutions to clustering, classification or density modelling problems using unlabeled data. SOM are efficiently used for the dimensionality reduction and for the visualisation of high-dimensional data (projection into a two-dimensional space). Unlabeled data are points/vectors in a high-dimensional feature space that have some attributes (or coordinates) but have no target values, neither continuous (as in a regression problem) nor discrete labels (as in the case of classification problem). The main task of SOM is to "group" or to "range" in some manner these input vectors and to try to catch regularities (to find patterns) in data by preserving topological structure and by using some well defined similarity measures. A generic methodology presented in this study consists of detailed spatial exploratory data analysis using statistical and geostatistical tools, analysis and modelling of spatial (cross)-correlations anisotropic structures, and application of SOM as a nonlinear modelling and visualisation tool. The case study considers multivariate data of sediments contamination by heavy metals (eight spatially distributes pollutants) in Geneva Lake. The most important modelling task is formulated as a problem of revealing structures or coherent clusters in this multivariate data set that would shed some light on the underlying phenomena of the contamination. Three major clusters, clearly spatially separated, were detected and explained by using the SOM technique.
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…
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…
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)
Method and Algorithm of Using Ontologies in E-Learning Sessions
NASA Astrophysics Data System (ADS)
Deliyska, Boryana; Manoilov, Peter
2009-11-01
In the article a method and algorithm of using ontologies in e-learning sessions is proposed. The method assumes utilization of software agents and domain and application ontologies. Software agents search, extract and submit learning objects to the learners. Depending on range and level of education, domain ontology of learner and application ontologies of curriculum, syllabus and learning object plans are used. A database of learner model is designed. Under conditions of adaptive learner-oriented e-learning an algorithm of navigation through content learning objects is composed. The algorithm includes dynamic calculation of possible routes of knowledge acquiring.
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…
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…
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.
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.
Modeling the Swift BAT Trigger Algorithm with Machine Learning
NASA Astrophysics Data System (ADS)
Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori
2016-02-01
To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of ≳97% (≲3% error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6% (10.4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of {n}0∼ {0.48}-0.23+0.41 {{{Gpc}}}-3 {{{yr}}}-1 with power-law indices of {n}1∼ {1.7}-0.5+0.6 and {n}2∼ -{5.9}-0.1+5.7 for GRBs above and below a break point of {z}1∼ {6.8}-3.2+2.8. This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.
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.
MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms
NASA Astrophysics Data System (ADS)
Albayrak, A.; Wei, J. C.; Petrenko, M.; Lary, D. J.; Leptoukh, G. G.
2011-12-01
Over the past decade, global aerosol observations have been conducted by space-borne sensors, airborne instruments, and ground-base network measurements. Unfortunately, quite often we encounter the differences of aerosol measurements by different well-calibrated instruments, even with a careful collocation in time and space. The differences might be rather substantial, and need to be better understood and accounted for when merging data from many sensors. The possible causes for these differences come from instrumental bias, different satellite viewing geometries, calibration issues, dynamically changing atmospheric and the surface conditions, and other "regressors", resulting in random and systematic errors in the final aerosol products. In this study, we will concentrate on the subject of removing biases and the systematic errors from MODIS (both Terra and Aqua) aerosol product, using Machine Learning algorithms. While we are assessing our regressors in our system when comparing global aerosol products, the Aerosol Robotic Network of sun-photometers (AERONET) will be used as a baseline for evaluating the MODIS aerosol products (Dark Target for land and ocean, and Deep Blue retrieval algorithms). The results of bias adjustment for MODIS Terra and Aqua are planned to be incorporated into the AeroStat Giovanni as part of the NASA ACCESS funded AeroStat project.
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.
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.
Effective and efficient optics inspection approach using machine learning algorithms
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.
Effective and efficient optics inspection approach using machine learning algorithms
NASA Astrophysics Data System (ADS)
Abdulla, Ghaleb M.; Kegelmeyer, Laura Mascio; Liao, Zhi M.; Carr, Wren
2010-11-01
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.
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…
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.
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…
Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm
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.
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…
Predicting mining activity with parallel genetic algorithms
Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.
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.
Forsström, J
1992-01-01
The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data. PMID:1551737
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.
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.
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…
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.
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. PMID:25893753
NASA Astrophysics Data System (ADS)
Singh, R.; Verma, H. K.
2013-12-01
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
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…
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,…
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…
Active Learning: The Way Children Construct Knowledge.
ERIC Educational Resources Information Center
Hohmann, Mary; Weikart, David P.
2002-01-01
The High/Scope approach to early childhood education promotes the belief that active learning is fundamental to the development of human potential and occurs most effectively in settings that provide developmentally appropriate learning opportunities. Describes five ingredients of active learning (materials, manipulation, choice, language from…
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…
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…
Reinforcement learning or active inference?
Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J
2009-01-01
This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain. PMID:19641614
Reinforcement Learning or Active Inference?
Friston, Karl J.; Daunizeau, Jean; Kiebel, Stefan J.
2009-01-01
This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain. PMID:19641614
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.
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.
An active set algorithm for nonlinear optimization with polyhedral constraints
NASA Astrophysics Data System (ADS)
Hager, William W.; Zhang, Hongchao
2016-08-01
A polyhedral active set algorithm PASA is developed for solving a nonlinear optimization problem whose feasible set is a polyhedron. Phase one of the algorithm is the gradient projection method, while phase two is any algorithm for solving a linearly constrained optimization problem. Rules are provided for branching between the two phases. Global convergence to a stationary point is established, while asymptotically PASA performs only phase two when either a nondegeneracy assumption holds, or the active constraints are linearly independent and a strong second-order sufficient optimality condition holds.
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.
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
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy
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
Ordering and finding the best of K > 2 supervised learning algorithms.
Yildiz, Olcay Taner; Alpaydin, Ethem
2006-03-01
Given a data set and a number of supervised learning algorithms, we would like to find the algorithm with the smallest expected error. Existing pairwise tests allow a comparison of two algorithms only; range tests and ANOVA check whether multiple algorithms have the same expected error and cannot be used for finding the smallest. We propose a methodology, the MultiTest algorithm, whereby we order supervised learning algorithms taking into account 1) the result of pairwise statistical tests on expected error (what the data tells us), and 2) our prior preferences, e.g., due to complexity. We define the problem in graph-theoretic terms and propose an algorithm to find the "best" learning algorithm in terms of these two criteria, or in the more general case, order learning algorithms in terms of their "goodness." Simulation results using five classification algorithms on 30 data sets indicate the utility of the method. Our proposed method can be generalized to regression and other loss functions by using a suitable pairwise test. PMID:16526425
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…
Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning
NASA Astrophysics Data System (ADS)
Xie, Weixing; Li, Wenhua; Gao, Xinbo
1995-08-01
Kohonen networks are well known for clustering analysis. Classical Kohonen networks for hard c-means clustering (trained by winner-take-all learning) have some severe drawbacks. Fuzzy Kohonen networks (FKCNN) for fuzzy c-means clustering are trained by fuzzy competition learning, and can get better clustering results than the classical Kohonen networks. However, both winner-take-all and fuzzy competition learning algorithms are in essence local search techniques that search for the optimum by using a hill-climbing technique. Thus, they often fail in the search for the global optimum. In this paper we combine genetic algorithms (GAs) with fuzzy competition learning to train the FKCNN. Our experimental results show that the proposed GA/FC learning algorithm has much higher probabilities of finding the global optimal solutions than either the winner-take-all or the fuzzy competition learning.
SAR ATR using a modified learning vector quantization algorithm
NASA Astrophysics Data System (ADS)
Marinelli, Anne Marie P.; Kaplan, Lance M.; Nasrabadi, Nasser M.
1999-08-01
We addressed the problem of classifying 10 target types in imagery formed from synthetic aperture radar (SAR). By executing a group training process, we show how to increase the performance of 10 initial sets of target templates formed by simple averaging. This training process is a modified learning vector quantization (LVQ) algorithm that was previously shown effective with forward-looking infrared (FLIR) imagery. For comparison, we ran the LVQ experiments using coarse, medium, and fine template sets that captured the target pose signature variations over 60 degrees, 40 degrees, and 20 degrees, respectively. Using sequestered test imagery, we evaluated how well the original and post-LVQ template sets classify the 10 target types. We show that after the LVQ training process, the coarse template set outperforms the coarse and medium original sets. And, for a test set that included untrained version variants, we show that classification using coarse template sets nearly matches that of the fine template sets. In a related experiment, we stored 9 initial template sets to classify 9 of the target types and used a threshold to separate the 10th type, previously found to be a 'confusing' type. We used imagery of all 10 targets in the LVQ training process to modify the 9 template sets. Overall classification performance increased slightly and an equalization of the individual target classification rates occurred, as compared to the 10-template experiment. The SAR imagery that we used is publicly available from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program, sponsored by the Defense Advanced Research Projects Agency (DARPA).
Modelling Typical Online Language Learning Activity
ERIC Educational Resources Information Center
Montoro, Carlos; Hampel, Regine; Stickler, Ursula
2014-01-01
This article presents the methods and results of a four-year-long research project focusing on the language learning activity of individual learners using online tasks conducted at the University of Guanajuato (Mexico) in 2009-2013. An activity-theoretical model (Blin, 2010; Engeström, 1987) of the typical language learning activity was used to…
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…
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.
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.
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…
An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.
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. PMID:25126605
Machine learning algorithms for damage detection: Kernel-based approaches
NASA Astrophysics Data System (ADS)
Santos, Adam; Figueiredo, Eloi; Silva, M. F. M.; Sales, C. S.; Costa, J. C. W. A.
2016-02-01
This paper presents four kernel-based algorithms for damage detection under varying operational and environmental conditions, namely based on one-class support vector machine, support vector data description, kernel principal component analysis and greedy kernel principal component analysis. Acceleration time-series from an array of accelerometers were obtained from a laboratory structure and used for performance comparison. The main contribution of this study is the applicability of the proposed algorithms for damage detection as well as the comparison of the classification performance between these algorithms and other four ones already considered as reliable approaches in the literature. All proposed algorithms revealed to have better classification performance than the previous ones.
Reinforcement active learning in the vibrissae system: optimal object localization.
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
An Active Sensor Algorithm for Corn Nitrogen Recommendations Based on a Chlorophyll Meter Algorithm
Technology Transfer Automated Retrieval System (TEKTRAN)
In previous work we found active canopy sensor reflectance assessments of corn (Zea mays L.) N status acquired at two growth stages (V11 and V15) have the greatest potential for directing in-season N applications, but emphasized an algorithm was needed to translate sensor readings into appropriate N...
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…
Adult Learning Principles in Designing Learning Activities for Teacher Development
ERIC Educational Resources Information Center
Gravani, Maria N.
2012-01-01
The research reported in this paper is an investigation of the application of adult learning principles in designing learning activities for teachers' life-long development. The exploration is illustrated by qualitative data from a case study of adult educators' and adult learners' insights and experiences of a teacher development course organised…
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…
Detection of suspicious activity using incremental outlier detection algorithms
NASA Astrophysics Data System (ADS)
Pokrajac, D.; Reljin, N.; Pejcic, N.; Vance, T.; McDaniel, S.; Lazarevic, A.; Chang, H. J.; Choi, J. Y.; Miezianko, R.
2009-08-01
Detection of unusual trajectories of moving objects can help in identifying suspicious activity on convoy routes and thus reduce casualties caused by improvised explosive devices. In this paper, using video imagery we compare efficiency of various techniques for incremental outlier detection on detecting unusual trajectories on simulated and real-life data obtained from SENSIAC database. Incremental outlier detection algorithms that we consider in this paper include incremental Support Vector Classifier (incSVC), incremental Local Outlier Factor (incLOF) algorithm and incremental Connectivity Outlier Factor (incCOF) algorithm. Our experiments performed on ground truth trajectory data indicate that incremental LOF algorithm can provide better detection of unusual trajectories in comparison to other examined techniques.
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
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…
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)
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…
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…
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…
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…
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. PMID:27173430
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.
Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine
Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong
2014-01-01
This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458
Test generation algorithm for fault detection of analog circuits based on extreme learning machine.
Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong
2014-01-01
This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458
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
An active foot lifter orthosis based on a PCPG algorithm.
Duvinage, Matthieu; Jiménez-Fábian, René; Castermans, Thierry; Verlinden, Olivier; Dutoit, Thierry
2011-01-01
Central pattern generators (CPGs) are known to play an important role in the generation of rhythmic movements in gait, both in animals and humans. The comprehension of their underlying mechanism has led to the development of an important family of algorithms at the basis of autonomous walking robots. Recently, it has been shown that human gait could be modeled using a subclass of those algorithms, namely a Programmable Central Pattern Generator (PCPG). In this paper, we present a foot lifter orthosis driven by this algorithm. After a learning phase, the PCPG is able to generate adequate rhythmic gait patterns both for constant speeds and acceleration phases. Its output is used to drive the orthosis actuator during the swing phase, in order to help patients suffering from foot drop (the orthosis just follows the movement during the stance phase). The most interesting property of this algorithm is the possibility to generate a smooth output signal even during speed transitions. In practice, given that human gait is not perfectly periodic, the phase of this signal needs to be reset with actual movement. Therefore, two phase-resetting procedures were studied: one standard hard phase-resetting leading to discontinuities and one original soft phase-resetting allowing to recover the correct phase in a smooth way. The simulation results and complete design of the orthosis hardware and software are presented. PMID:22275540
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.
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
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.
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
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm
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
Reconstructing Causal Biological Networks through Active Learning.
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
Reconstructing Causal Biological Networks through Active Learning
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
Baba, Norio; Mogami, Yoshio
2006-08-01
A new learning algorithm for the hierarchical structure learning automata (HSLA) operating in the nonstationary multiteacher environment (NME) is proposed. The proposed algorithm is derived by extending the original relative reward-strength algorithm to be utilized in the HSLA operating in the general NME. It is shown that the proposed algorithm ensures convergence with probability 1 to the optimal path under a certain type of the NME. Several computer-simulation results, which have been carried out in order to compare the relative performance of the proposed algorithm in some NMEs against those of the two of the fastest algorithms today, confirm the effectiveness of the proposed algorithm. PMID:16903364
Single-Iteration Learning Algorithm for Feed-Forward Neural Networks
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.
SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM
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.
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
A cross-validation scheme for machine learning algorithms in shotgun proteomics
2012-01-01
Peptides are routinely identified from mass spectrometry-based proteomics experiments by matching observed spectra to peptides derived from protein databases. The error rates of these identifications can be estimated by target-decoy analysis, which involves matching spectra to shuffled or reversed peptides. Besides estimating error rates, decoy searches can be used by semi-supervised machine learning algorithms to increase the number of confidently identified peptides. As for all machine learning algorithms, however, the results must be validated to avoid issues such as overfitting or biased learning, which would produce unreliable peptide identifications. Here, we discuss how the target-decoy method is employed in machine learning for shotgun proteomics, focusing on how the results can be validated by cross-validation, a frequently used validation scheme in machine learning. We also use simulated data to demonstrate the proposed cross-validation scheme's ability to detect overfitting. PMID:23176259
A second-order learning algorithm for multilayer networks based on block Hessian matrix.
Wang, Yi Jen; Lin, Chin Teng
1998-12-01
This article proposes a new second-order learning algorithm for training the multilayer perceptron (MLP) networks. The proposed algorithm is a revised Newton's method. A forward-backward propagation scheme is first proposed for network computation of the Hessian matrix, H, of the output error function of the MLP. A block Hessian matrix, H(b), is then defined to approximate and simplify H. Several lemmas and theorems are proved to uncover the important properties of H and H(b), and verify the good approximation of H(b) to H; H(b) preserves the major properties of H. The theoretic analysis leads to the development of an efficient way for computing the inverse of H(b) recursively. In the proposed second-order learning algorithm, the least squares estimation technique is adopted to further lessen the local minimum problems. The proposed algorithm overcomes not only the drawbacks of the standard backpropagation algorithm (i.e. slow asymptotic convergence rate, bad controllability of convergence accuracy, local minimum problems, and high sensitivity to learning constant), but also the shortcomings of normal Newton's method used on the MLP, such as the lack of network implementation of H, ill representability of the diagonal terms of H, the heavy computation load of the inverse of H, and the requirement of a good initial estimate of the solution (weights). Several example problems are used to demonstrate the efficiency of the proposed learning algorithm. Extensive performance (convergence rate and accuracy) comparisons of the proposed algorithm with other learning schemes (including the standard backpropagation algorithm) are also made. PMID:12662732
A robust regularization algorithm for polynomial networks for machine learning
NASA Astrophysics Data System (ADS)
Jaenisch, Holger M.; Handley, James W.
2011-06-01
We present an improvement to the fundamental Group Method of Data Handling (GMDH) Data Modeling algorithm that overcomes the parameter sensitivity to novel cases presented to derived networks. We achieve this result by regularization of the output and using a genetic weighting that selects intermediate models that do not exhibit divergence. The result is the derivation of multi-nested polynomial networks following the Kolmogorov-Gabor polynomial that are robust to mean estimators as well as novel exemplars for input. The full details of the algorithm are presented. We also introduce a new method for approximating GMDH in a single regression model using F, H, and G terms that automatically exports the answers as ordinary differential equations. The MathCAD 15 source code for all algorithms and results are provided.
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.
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…
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.…
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…
Incorporating Active Learning into a Traditional Curriculum.
ERIC Educational Resources Information Center
Carroll, Robert G.; Huang, Alice H.
1997-01-01
Discusses self-learning exercises (SLEs) incorporated into the Medical Physiology course for first-year students at the Morehouse School of Medicine in Atlanta, GA. Twenty to thirty percent of course material is presented in these exercises instead of in lectures. The exercises develop active learning and problem-solving skills. Formal analysis…
61 Cooperative Learning Activities in ESL.
ERIC Educational Resources Information Center
Hirsch, Charles; Supple, Deborah Beres
Cooperative learning activities, instructional strategies, and reproducible classroom materials are provided to assist teachers with English-as-a-Second-Language learners in their classes. They are designed to help students develop English language skills using conversation-based cooperative learning principles, with native speakers and ESL…
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,…
A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem
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.
Dobkin, Bruce H.; Xu, Xiaoyu; Batalin, Maxim; Thomas, Seth; Kaiser, William
2015-01-01
Background and Purpose Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. Methods Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. Results A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. Conclusions Test–retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies. PMID:21636815
"Active Learning for Active Citizenship": Democratic Citizenship and Lifelong Learning
ERIC Educational Resources Information Center
Annette, John
2009-01-01
This article explores to what extent citizenship education for lifelong learning should be based on a more "political" or civic republican conception of citizenship as compared to a liberal individualist conception, which emphasizes individual rights, or a communitarian conception, which emphasizes moral and social responsibilities. It also…
Genetic algorithms: What computers can learn from Darwin
Walbridge, C.T. )
1989-01-01
In this article the author posits a field of computing based on the genetic algorithm. This approach to programming mimics evolution by utilizing a computer to solve problems on a trial and error basis and ascertain the best answer through natural selection of the best of the computer's guesses. The author discusses the viability of this system in comparison to that of artificial intelligence.
Spectral Regularization Algorithms for Learning Large Incomplete Matrices.
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. PMID:21552465
Learning interpretive decision algorithm for severe storm forecasting support
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.
Applying active learning to assertion classification of concepts in clinical text.
Chen, Yukun; Mani, Subramani; Xu, Hua
2012-04-01
Supervised machine learning methods for clinical natural language processing (NLP) research require a large number of annotated samples, which are very expensive to build because of the involvement of physicians. Active learning, an approach that actively samples from a large pool, provides an alternative solution. Its major goal in classification is to reduce the annotation effort while maintaining the quality of the predictive model. However, few studies have investigated its uses in clinical NLP. This paper reports an application of active learning to a clinical text classification task: to determine the assertion status of clinical concepts. The annotated corpus for the assertion classification task in the 2010 i2b2/VA Clinical NLP Challenge was used in this study. We implemented several existing and newly developed active learning algorithms and assessed their uses. The outcome is reported in the global ALC score, based on the Area under the average Learning Curve of the AUC (Area Under the Curve) score. Results showed that when the same number of annotated samples was used, active learning strategies could generate better classification models (best ALC-0.7715) than the passive learning method (random sampling) (ALC-0.7411). Moreover, to achieve the same classification performance, active learning strategies required fewer samples than the random sampling method. For example, to achieve an AUC of 0.79, the random sampling method used 32 samples, while our best active learning algorithm required only 12 samples, a reduction of 62.5% in manual annotation effort. PMID:22127105
Point-of-Purchase Advertising. Learning Activity.
ERIC Educational Resources Information Center
Shackelford, Ray
1998-01-01
In this technology education activity, students learn the importance of advertising, conduct a day-long survey of advertising strategies, and design and produce a tabletop point-of-purchase advertisement. (JOW)
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)
Learning to play like a human: case injected genetic algorithms for strategic computer gaming
NASA Astrophysics Data System (ADS)
Louis, Sushil J.; Miles, Chris
2006-05-01
We use case injected genetic algorithms to learn how to competently play computer strategy games that involve long range planning across complex dynamics. Imperfect knowledge presented to players requires them adapt their strategies in order to anticipate opponent moves. We focus on the problem of acquiring knowledge learned from human players, in particular we learn general routing information from a human player in the context of a strike force planning game. By incorporating case injection into a genetic algorithm, we show methods for incorporating general knowledge elicited from human players into future plans. In effect allowing the GA to take important strategic elements from human play and merging those elements into its own strategic thinking. Results show that with an appropriate representation, case injection is effective at biasing the genetic algorithm toward producing plans that contain important strategic elements used by human players.
Dopamine, reward learning, and active inference
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
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.
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.
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…
Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information.
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. PMID:25706894
A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning.
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. PMID:25216485
Thermodynamically weighted ART (THWART): a finite-temperature activated algorithm
NASA Astrophysics Data System (ADS)
Barkema, Gerard; Mousseau, Normand
2004-03-01
Much effort has been invested in the last decade to develop accelerated algorithms. Many of these methods are limited either by having to work effectively at T=0 (ART, eigenvector-following or dimer method) or by the complexity level of the system under study (hyper-MD, TAD, etc.). The thermodynamically weighted activation-relaxation technique (THWART) overcomes some of these limitations. Coupling molecular dynamics with ART, this algorithm can be shown to sample the configurational space with the correct ensemble, while generating a trajectory that can go over large activation barriers. Preliminary simulations show that the method is many orders of magnitude faster than MD for sampling the configurational space of amorphous silicon at T=800 K and small peptides at 300 K. This work is supported in part by NSERC (Canada) and FRQNT (Québec). The simulations were performed on the supercomputers of the RQCHP. NM is a Cottrell Scholar of the Research Corporation.
PDT: Photometric DeTrending Algorithm Using Machine Learning
NASA Astrophysics Data System (ADS)
Kim, Dae-Won
2016-05-01
PDT removes systematic trends in light curves. It finds clusters of light curves that are highly correlated using machine learning, constructs one master trend per cluster and detrends an individual light curve using the constructed master trends by minimizing residuals while constraining coefficients to be positive.
Going the Distance: Active Learning.
ERIC Educational Resources Information Center
Notar, Charles E.; Restauri, Sherri; Wilson, Janell D.; Friery, Kathleen A.
The growth and development of distance learning (DL) programs is on the rise. This review examines the literature looking for instructional techniques and methods for the teacher desiring to use DL technology to maximize student achievement and cognitive development and to increase student interaction. The three major relationships within the…
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…
Correlates of reward-predictive value in learning-related hippocampal neural activity
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
Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly A
2013-02-15
High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652
Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly
2013-01-01
High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652
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
Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm.
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
Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
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
NASA Astrophysics Data System (ADS)
Rao, R. V.; Savsani, V. J.; Balic, J.
2012-12-01
An efficient optimization algorithm called teaching-learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, ɛ-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.
Actively learning object names across ambiguous situations.
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
How tracer objects can improve competitive learning algorithms in astronomy
NASA Astrophysics Data System (ADS)
Hernandez-Pajares, M.; Floris, J.; Murtagh, F.
The main objective of this paper is to discuss how the use of tracer objects in competitive learning can improve results in stellar classification. To do this, we work with a Kohonen network applied to a reduced sample of the Hipparcos Input Catalogue, which contains missing values. The use of synthetic stars as tracer objects allows us to determine the discrimination quality and to find the best final values of the cluster centroids, or neuron weights.
Experienced Teachers' Informal Learning: Learning Activities and Changes in Behavior and Cognition
ERIC Educational Resources Information Center
Hoekstra, Annemarieke; Brekelmans, Mieke; Beijaard, Douwe; Korthagen, Fred
2009-01-01
In this study on 32 teachers' learning in an informal learning environment, we analyzed changes in conceptions and behavior regarding students' active and self-regulated learning (ASL), and relations with the teachers' learning activities. Few relations were found between observed changes in "behavior" and learning activities. Changes in…
Active Semi-Supervised Learning Method with Hybrid Deep Belief Networks
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
Active semi-supervised learning method with hybrid deep belief networks.
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
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.
Implementing a Gaussian Process Learning Algorithm in Mixed Parallel Environment
Chandola, Varun; Vatsavai, Raju
2011-01-01
In this paper, we present a scalability analysis of a parallel Gaussian process training algorithm to simultaneously analyze a massive number of time series. We study three different parallel implementations: using threads, MPI, and a hybrid implementation using threads and MPI. We compare the scalability for the multi-threaded implementation on three different hardware platforms: a Mac desktop with two quad-core Intel Xeon processors (16 virtual cores), a Linux cluster node with four quad-core 2.3 GHz AMD Opteron processors, and SGI Altix ICE 8200 cluster node with two quad-core Intel Xeon processors (16 virtual cores). We also study the scalability of the MPI based and the hybrid MPI and thread based implementations on the SGI cluster with 128 nodes (2048 cores). Experimental results show that the hybrid implementation scales better than the multi-threaded and MPI based implementations. The hybrid implementation, using 1536 cores, can analyze a remote sensing data set with over 4 million time series in nearly 5 seconds while the serial algorithm takes nearly 12 hours to process the same data set.
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.
NASA Astrophysics Data System (ADS)
Lin, Wenwen; Yu, D. Y.; Wang, S.; Zhang, Chaoyong; Zhang, Sanqiang; Tian, Huiyu; Luo, Min; Liu, Shengqiang
2015-07-01
In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching-learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.
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…
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
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…
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…
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)…
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…
Lu, Y; Sundararajan, N; Saratchandran, P
1998-01-01
This paper presents a detailed performance analysis of the minimal resource allocation network (M-RAN) learning algorithm, M-RAN is a sequential learning radial basis function neural network which combines the growth criterion of the resource allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RAN. The performance of this algorithm is compared with the multilayer feedforward networks (MFNs) trained with 1) a variant of the standard backpropagation algorithm, known as RPROP and 2) the dependence identification (DI) algorithm of Moody and Antsaklis on several benchmark problems in the function approximation and pattern classification areas. For all these problems, the M-RAN algorithm is shown to realize networks with far fewer hidden neurons with better or same approximation/classification accuracy. Further, the time taken for learning (training) is also considerably shorter as M-RAN does not require repeated presentation of the training data. PMID:18252454
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...
Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality
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
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…
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).
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.…
Bayesian network structure learning based on the chaotic particle swarm optimization algorithm.
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
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.
Emotion Estimation Algorithm from Facial Image Analyses of e-Learning Users
NASA Astrophysics Data System (ADS)
Shigeta, Ayuko; Koike, Takeshi; Kurokawa, Tomoya; Nosu, Kiyoshi
This paper proposes an emotion estimation algorithm from e-Learning user's facial image. The algorithm characteristics are as follows: The criteria used to relate an e-Learning use's emotion to a representative emotion were obtained from the time sequential analysis of user's facial expressions. By examining the emotions of the e-Learning users and the positional change of the facial expressions from the experiment results, the following procedures are introduce to improve the estimation reliability; (1) some effective features points are chosen by the emotion estimation (2) dividing subjects into two groups by the change rates of the face feature points (3) selection of the eigenvector of the variance-co-variance matrices (cumulative contribution rate>=95%) (4) emotion calculation using Mahalanobis distance.
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.
A stochastic learning algorithm for layered neural networks
Bartlett, E.B.; Uhrig, R.E.
1992-12-31
The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.
A stochastic learning algorithm for layered neural networks
Bartlett, E.B. . Dept. of Mechanical Engineering); Uhrig, R.E. . Dept. of Nuclear Engineering)
1992-01-01
The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.
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.
Active Learning Strategies to Promote Critical Thinking
2003-01-01
Objective: To provide a brief introduction to the definition and disposition to think critically along with active learning strategies to promote critical thinking. Data Sources: I searched MEDLINE and Educational Resources Information Center (ERIC) from 1933 to 2002 for literature related to critical thinking, the disposition to think critically, questioning, and various critical-thinking pedagogic techniques. Data Synthesis: The development of critical thinking has been the topic of many educational articles recently. Numerous instructional methods exist to promote thought and active learning in the classroom, including case studies, discussion methods, written exercises, questioning techniques, and debates. Three methods—questioning, written exercises, and discussion and debates—are highlighted. Conclusions/Recommendations: The definition of critical thinking, the disposition to think critically, and different teaching strategies are featured. Although not appropriate for all subject matter and classes, these learning strategies can be used and adapted to facilitate critical thinking and active participation. PMID:16558680
Learning plan applicability through active mental entities
Baroni, Pietro; Fogli, Daniela; Guida, Giovanni
1999-03-22
This paper aims at laying down the foundations of a new approach to learning in autonomous mobile robots. It is based on the assumption that robots can be provided with built-in action plans and with mechanisms to modify and improve such plans. This requires that robots are equipped with some form of high-level reasoning capabilities. Therefore, the proposed learning technique is embedded in a novel distributed control architecture featuring an explicit model of robot's cognitive activity. In particular, cognitive activity is obtained by the interaction of active mental entities, such as intentions, persuasions and expectations. Learning capabilities are implemented starting from the interaction of such mental entities. The proposal is illustrated through an example concerning a robot in charge of reaching a target in an unknown environment cluttered with obstacles.
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.
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
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…
Restoration algorithms and system performance evaluation for active imagers
NASA Astrophysics Data System (ADS)
Gilles, Jérôme
2007-10-01
This paper deals with two fields related to active imaging system. First, we begin to explore image processing algorithms to restore the artefacts like speckle, scintillation and image dancing caused by atmospheric turbulence. Next, we examine how to evaluate the performance of this kind of systems. To do this task, we propose a modified version of the german TRM3 metric which permits to get MTF-like measures. We use the database acquired during NATO-TG40 field trials to make our tests.