Making the error-controlling algorithm of observable operator models constructive.
Zhao, Ming-Jie; Jaeger, Herbert; Thon, Michael
2009-12-01
Observable operator models (OOMs) are a class of models for stochastic processes that properly subsumes the class that can be modeled by finite-dimensional hidden Markov models (HMMs). One of the main advantages of OOMs over HMMs is that they admit asymptotically correct learning algorithms. A series of learning algorithms has been developed, with increasing computational and statistical efficiency, whose recent culmination was the error-controlling (EC) algorithm developed by the first author. The EC algorithm is an iterative, asymptotically correct algorithm that yields (and minimizes) an assured upper bound on the modeling error. The run time is faster by at least one order of magnitude than EM-based HMM learning algorithms and yields significantly more accurate models than the latter. Here we present a significant improvement of the EC algorithm: the constructive error-controlling (CEC) algorithm. CEC inherits from EC the main idea of minimizing an upper bound on the modeling error but is constructive where EC needs iterations. As a consequence, we obtain further gains in learning speed without loss in modeling accuracy.
An e-Learning environment for algorithmic: toward an active construction of skills
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Abdelghani Babori
2016-07-01
Full Text Available Assimilating an algorithmic course is a persistent problem for many undergraduate students. The major problem faced by students is the lack of problem solving ability and flexibility. Therefore, students are generally passive, unmotivated and unable to mobilize all the acquired knowledge (loops, test, variables, etc. to deal with new encountered problems. Our study is structured around building, step by step, problem solving skills among novice learners. Our approach is based on the use of problem based learning in an e-Learning environment. We begin by establishing a cognitive model which represents knowledge elements, grouped into categories of skills, judged necessary to be appropriated. We then propose a problem built on a concrete situation which aims to actively construct a skill category. We conclude by presenting around the proposed problem a pedagogical scenario for the set of learning activities designed to be incorporated in an E-learning platform.
Learning sorting algorithms through visualization construction
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
A Decomposition Algorithm for Learning Bayesian Network Structures from Data
DEFF Research Database (Denmark)
Zeng, Yifeng; Cordero Hernandez, Jorge
2008-01-01
It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....
Cascade Error Projection: An Efficient Hardware Learning Algorithm
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.
Online learning algorithm for ensemble of decision rules
Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2011-01-01
We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach
Online learning algorithm for ensemble of decision rules
Chikalov, Igor
2011-01-01
We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach. © 2011 Springer-Verlag.
Comparison of greedy algorithms for α-decision tree construction
Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail
2011-01-01
A comparison among different heuristics that are used by greedy algorithms which constructs approximate decision trees (α-decision trees) is presented. The comparison is conducted using decision tables based on 24 data sets from UCI Machine Learning Repository [2]. Complexity of decision trees is estimated relative to several cost functions: depth, average depth, number of nodes, number of nonterminal nodes, and number of terminal nodes. Costs of trees built by greedy algorithms are compared with minimum costs calculated by an algorithm based on dynamic programming. The results of experiments assign to each cost function a set of potentially good heuristics that minimize it. © 2011 Springer-Verlag.
Machine Learning in Production Systems Design Using Genetic Algorithms
Abu Qudeiri Jaber; Yamamoto Hidehiko Rizauddin Ramli
2008-01-01
To create a solution for a specific problem in machine learning, the solution is constructed from the data or by use a search method. Genetic algorithms are a model of machine learning that can be used to find nearest optimal solution. While the great advantage of genetic algorithms is the fact that they find a solution through evolution, this is also the biggest disadvantage. Evolution is inductive, in nature life does not evolve towards a good solution but it evolves aw...
Unsupervised learning algorithms
Aydin, Kemal
2016-01-01
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering,...
Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid
2017-10-01
One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.
Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro
1995-02-01
We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.
Algorithms for Decision Tree Construction
Chikalov, Igor
2011-01-01
The study of algorithms for decision tree construction was initiated in 1960s. The first algorithms are based on the separation heuristic [13, 31] that at each step tries dividing the set of objects as evenly as possible. Later Garey and Graham [28] showed that such algorithm may construct decision trees whose average depth is arbitrarily far from the minimum. Hyafil and Rivest in [35] proved NP-hardness of DT problem that is constructing a tree with the minimum average depth for a diagnostic problem over 2-valued information system and uniform probability distribution. Cox et al. in [22] showed that for a two-class problem over information system, even finding the root node attribute for an optimal tree is an NP-hard problem. © Springer-Verlag Berlin Heidelberg 2011.
Robust Semi-Supervised Manifold Learning Algorithm for Classification
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Mingxia Chen
2018-01-01
Full Text Available In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional structures of the high-dimensional data. Given partially labeled data, the semi-supervised manifold learning algorithms are proposed to predict the labels of the unlabeled points, taking into account label information. However, these semi-supervised manifold learning algorithms are not robust against noisy points, especially when the labeled data contain noise. In this paper, we propose a framework for robust semi-supervised manifold learning (RSSML to address this problem. The noisy levels of the labeled points are firstly predicted, and then a regularization term is constructed to reduce the impact of labeled points containing noise. A new robust semi-supervised optimization model is proposed by adding the regularization term to the traditional semi-supervised optimization model. Numerical experiments are given to show the improvement and efficiency of RSSML on noisy data sets.
Algorithms for Reinforcement Learning
Szepesvari, Csaba
2010-01-01
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'
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Weilin Nie
2017-01-01
Full Text Available Abstract Convex risk minimization is a commonly used setting in learning theory. In this paper, we firstly give a perturbation analysis for such algorithms, and then we apply this result to differential private learning algorithms. Our analysis needs the objective functions to be strongly convex. This leads to an extension of our previous analysis to the non-differentiable loss functions, when constructing differential private algorithms. Finally, an error analysis is then provided to show the selection for the parameters.
Algorithms for Decision Tree Construction
Chikalov, Igor
2011-01-01
The study of algorithms for decision tree construction was initiated in 1960s. The first algorithms are based on the separation heuristic [13, 31] that at each step tries dividing the set of objects as evenly as possible. Later Garey and Graham [28
Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning
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Guangyi Liu
2014-01-01
Full Text Available Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.
Machine Learning an algorithmic perspective
Marsland, Stephen
2009-01-01
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le
Cascade Error Projection Learning Algorithm
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.
Quantum learning algorithms for quantum measurements
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Bisio, Alessandro, E-mail: alessandro.bisio@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); D' Ariano, Giacomo Mauro, E-mail: dariano@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Perinotti, Paolo, E-mail: paolo.perinotti@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Sedlak, Michal, E-mail: michal.sedlak@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Institute of Physics, Slovak Academy of Sciences, Dubravska cesta 9, 845 11 Bratislava (Slovakia)
2011-09-12
We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.
Quantum learning algorithms for quantum measurements
International Nuclear Information System (INIS)
Bisio, Alessandro; D'Ariano, Giacomo Mauro; Perinotti, Paolo; Sedlak, Michal
2011-01-01
We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.
Azad, Mohammad
2013-11-25
In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.
Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2013-01-01
In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.
Unconventional Algorithms: Complementarity of Axiomatics and Construction
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Gordana Dodig Crnkovic
2012-10-01
Full Text Available In this paper, we analyze axiomatic and constructive issues of unconventional computations from a methodological and philosophical point of view. We explain how the new models of algorithms and unconventional computations change the algorithmic universe, making it open and allowing increased flexibility and expressive power that augment creativity. At the same time, the greater power of new types of algorithms also results in the greater complexity of the algorithmic universe, transforming it into the algorithmic multiverse and demanding new tools for its study. That is why we analyze new powerful tools brought forth by local mathematics, local logics, logical varieties and the axiomatic theory of algorithms, automata and computation. We demonstrate how these new tools allow efficient navigation in the algorithmic multiverse. Further work includes study of natural computation by unconventional algorithms and constructive approaches.
Empirical tests of the Gradual Learning Algorithm
Boersma, P.; Hayes, B.
1999-01-01
The Gradual Learning Algorithm (Boersma 1997) is a constraint ranking algorithm for learning Optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and
Empirical tests of the Gradual Learning Algorithm
Boersma, P.; Hayes, B.
2001-01-01
The Gradual Learning Algorithm (Boersma 1997) is a constraint-ranking algorithm for learning optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and
Learning algorithms and automatic processing of languages
International Nuclear Information System (INIS)
Fluhr, Christian Yves Andre
1977-01-01
This research thesis concerns the field of artificial intelligence. It addresses learning algorithms applied to automatic processing of languages. The author first briefly describes some mechanisms of human intelligence in order to describe how these mechanisms are simulated on a computer. He outlines the specific role of learning in various manifestations of intelligence. Then, based on the Markov's algorithm theory, the author discusses the notion of learning algorithm. Two main types of learning algorithms are then addressed: firstly, an 'algorithm-teacher dialogue' type sanction-based algorithm which aims at learning how to solve grammatical ambiguities in submitted texts; secondly, an algorithm related to a document system which structures semantic data automatically obtained from a set of texts in order to be able to understand by references to any question on the content of these texts
A new algorithm to construct phylogenetic networks from trees.
Wang, J
2014-03-06
Developing appropriate methods for constructing phylogenetic networks from tree sets is an important problem, and much research is currently being undertaken in this area. BIMLR is an algorithm that constructs phylogenetic networks from tree sets. The algorithm can construct a much simpler network than other available methods. Here, we introduce an improved version of the BIMLR algorithm, QuickCass. QuickCass changes the selection strategy of the labels of leaves below the reticulate nodes, i.e., the nodes with an indegree of at least 2 in BIMLR. We show that QuickCass can construct simpler phylogenetic networks than BIMLR. Furthermore, we show that QuickCass is a polynomial-time algorithm when the output network that is constructed by QuickCass is binary.
A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition
Zhang, Wei; Zhang, Xueying; Sun, Ying
2017-01-01
Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between e...
Learning theory of distributed spectral algorithms
International Nuclear Information System (INIS)
Guo, Zheng-Chu; Lin, Shao-Bo; Zhou, Ding-Xuan
2017-01-01
Spectral algorithms have been widely used and studied in learning theory and inverse problems. This paper is concerned with distributed spectral algorithms, for handling big data, based on a divide-and-conquer approach. We present a learning theory for these distributed kernel-based learning algorithms in a regression framework including nice error bounds and optimal minimax learning rates achieved by means of a novel integral operator approach and a second order decomposition of inverse operators. Our quantitative estimates are given in terms of regularity of the regression function, effective dimension of the reproducing kernel Hilbert space, and qualification of the filter function of the spectral algorithm. They do not need any eigenfunction or noise conditions and are better than the existing results even for the classical family of spectral algorithms. (paper)
Storage capacity of the Tilinglike Learning Algorithm
International Nuclear Information System (INIS)
Buhot, Arnaud; Gordon, Mirta B.
2001-01-01
The storage capacity of an incremental learning algorithm for the parity machine, the Tilinglike Learning Algorithm, is analytically determined in the limit of a large number of hidden perceptrons. Different learning rules for the simple perceptron are investigated. The usual Gardner-Derrida rule leads to a storage capacity close to the upper bound, which is independent of the learning algorithm considered
Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K
2015-01-01
Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (pmachine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273
WDM Multicast Tree Construction Algorithms and Their Comparative Evaluations
Makabe, Tsutomu; Mikoshi, Taiju; Takenaka, Toyofumi
We propose novel tree construction algorithms for multicast communication in photonic networks. Since multicast communications consume many more link resources than unicast communications, effective algorithms for route selection and wavelength assignment are required. We propose a novel tree construction algorithm, called the Weighted Steiner Tree (WST) algorithm and a variation of the WST algorithm, called the Composite Weighted Steiner Tree (CWST) algorithm. Because these algorithms are based on the Steiner Tree algorithm, link resources among source and destination pairs tend to be commonly used and link utilization ratios are improved. Because of this, these algorithms can accept many more multicast requests than other multicast tree construction algorithms based on the Dijkstra algorithm. However, under certain delay constraints, the blocking characteristics of the proposed Weighted Steiner Tree algorithm deteriorate since some light paths between source and destinations use many hops and cannot satisfy the delay constraint. In order to adapt the approach to the delay-sensitive environments, we have devised the Composite Weighted Steiner Tree algorithm comprising the Weighted Steiner Tree algorithm and the Dijkstra algorithm for use in a delay constrained environment such as an IPTV application. In this paper, we also give the results of simulation experiments which demonstrate the superiority of the proposed Composite Weighted Steiner Tree algorithm compared with the Distributed Minimum Hop Tree (DMHT) algorithm, from the viewpoint of the light-tree request blocking.
Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan
2017-12-20
A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.
Kernel learning algorithms for face recognition
Li, Jun-Bao; Pan, Jeng-Shyang
2013-01-01
Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new
KARATE WITH CONSTRUCTIVE LEARNING
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Srikrishna Karanam
2012-02-01
Full Text Available Any conventional learning process involves the traditional hierarchy of garnering of information and then recall gathered information. Constructive learning is an important research area having wide impact on teaching methods in education, learning theories, and plays a major role in many education reform movements. It is observed that constructive learning advocates the interconnection between emotions and learning. Human teachers identify the emotions of students with varying degrees of accuracy and can improve the learning rate of the students by motivating them. In learning with computers, computers also should be given the capability to recognize emotions so as to optimize the learning process. Image Processing is a very popular tool used in the process of establishing the theory of Constructive Learning. In this paper we use the Optical Flow computation in image sequences to analyze the accuracy of the moves of a karate player. We have used the Lucas-Kanade method for computing the optical flow in image sequences. A database consisting of optical flow images by a group of persons learning karate is formed and the learning rates are analyzed in order to main constructive learning. The contours of flow images are compared with the standard images and the error graphs are plotted. Analysis of the emotion of the amateur karate player is made by observing the error plots.
Construction Example for Algebra System Using Harmony Search Algorithm
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FangAn Deng
2015-01-01
Full Text Available The construction example of algebra system is to verify the existence of a complex algebra system, and it is a NP-hard problem. In this paper, to solve this kind of problems, firstly, a mathematical optimization model for construction example of algebra system is established. Secondly, an improved harmony search algorithm based on NGHS algorithm (INGHS is proposed to find as more solutions as possible for the optimization model; in the proposed INGHS algorithm, to achieve the balance between exploration power and exploitation power in the search process, a global best strategy and parameters dynamic adjustment method are present. Finally, nine construction examples of algebra system are used to evaluate the optimization model and performance of INGHS. The experimental results show that the proposed algorithm has strong performance for solving complex construction example problems of algebra system.
Constructing a graph of connections in clustering algorithm of complex objects
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Татьяна Шатовская
2015-05-01
Full Text Available The article describes the results of modifying the algorithm Chameleon. Hierarchical multi-level algorithm consists of several phases: the construction of the count, coarsening, the separation and recovery. Each phase can be used various approaches and algorithms. The main aim of the work is to study the quality of the clustering of different sets of data using a set of algorithms combinations at different stages of the algorithm and improve the stage of construction by the optimization algorithm of k choice in the graph construction of k of nearest neighbors
Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate
International Nuclear Information System (INIS)
Lv Jiancheng; Yi Zhang
2007-01-01
The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm
Energy Technology Data Exchange (ETDEWEB)
Fluhr, Christian Yves Andre
1977-06-15
This research thesis concerns the field of artificial intelligence. It addresses learning algorithms applied to automatic processing of languages. The author first briefly describes some mechanisms of human intelligence in order to describe how these mechanisms are simulated on a computer. He outlines the specific role of learning in various manifestations of intelligence. Then, based on the Markov's algorithm theory, the author discusses the notion of learning algorithm. Two main types of learning algorithms are then addressed: firstly, an 'algorithm-teacher dialogue' type sanction-based algorithm which aims at learning how to solve grammatical ambiguities in submitted texts; secondly, an algorithm related to a document system which structures semantic data automatically obtained from a set of texts in order to be able to understand by references to any question on the content of these texts.
Azad, Mohammad
2016-10-20
The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.
Azad, Mohammad; Moshkov, Mikhail; Zielosko, Beata
2016-01-01
The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.
A Newton-type neural network learning algorithm
International Nuclear Information System (INIS)
Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.
1993-01-01
First- and second-order learning methods for feed-forward multilayer networks are considered. A Newton-type algorithm is proposed and compared with the common back-propagation algorithm. It is shown that the proposed algorithm provides better learning quality. Some recommendations for their usage are given. 11 refs.; 1 fig.; 1 tab
Load Balancing Issues with Constructing Phylogenetic Trees using Neighbour-Joining Algorithm
International Nuclear Information System (INIS)
Al Mamun, S M
2012-01-01
Phylogenetic tree construction is one of the most important and interesting problems in bioinformatics. Constructing an efficient phylogenetic tree has always been a research issue. It needs to consider both the correctness and the speed of the tree construction. In this paper, we implemented the neighbour-joining algorithm, using Message Passing Interface (MPI) for constructing the phylogenetic tree. Performance is efficacious, comparing to the best sequential algorithm. From this paper, it would be clear to the researchers that how load balance can make a great effect for constructing phylogenetic trees using neighbour-joining 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.
A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition
Directory of Open Access Journals (Sweden)
Wei Zhang
2017-01-01
Full Text Available Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps. Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%–20% better than those of single features. The proposed FCM learning algorithm generates 5%–20% performance improvement compared with traditional classification networks.
Validating module network learning algorithms using simulated data.
Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves
2007-05-03
In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network
An algorithm for constructing Lyapunov functions
Directory of Open Access Journals (Sweden)
Sigurdur Freyr Hafstein
2007-08-01
Full Text Available In this monograph we develop an algorithm for constructing Lyapunov functions for arbitrary switched dynamical systems $dot{mathbf{x}} = mathbf{f}_sigma(t,mathbf{x}$, possessing a uniformly asymptotically stable equilibrium. Let $dot{mathbf{x}}=mathbf{f}_p(t,mathbf{x}$, $pinmathcal{P}$, be the collection of the ODEs, to which the switched system corresponds. The number of the vector fields $mathbf{f}_p$ on the right-hand side of the differential equation is assumed to be finite and we assume that their components $f_{p,i}$ are $mathcal{C}^2$ functions and that we can give some bounds, not necessarily close, on their second-order partial derivatives. The inputs of the algorithm are solely a finite number of the function values of the vector fields $mathbf{f}_p$ and these bounds. The domain of the Lyapunov function constructed by the algorithm is only limited by the size of the equilibrium's region of attraction. Note, that the concept of a Lyapunov function for the arbitrary switched system $dot{mathbf{x}} = mathbf{f}_sigma(t,mathbf{x}$ is equivalent to the concept of a common Lyapunov function for the systems $dot{mathbf{x}}=mathbf{f}_p(t,mathbf{x}$, $pinmathcal{P}$, and that if $mathcal{P}$ contains exactly one element, then the switched system is just a usual ODE $dot{mathbf{x}}=mathbf{f}(t,mathbf{x}$. We give numerous examples of Lyapunov functions constructed by our method at the end of this monograph.
"Accelerated Perceptron": A Self-Learning Linear Decision Algorithm
Zuev, Yu. A.
2003-01-01
The class of linear decision rules is studied. A new algorithm for weight correction, called an "accelerated perceptron", is proposed. In contrast to classical Rosenblatt's perceptron this algorithm modifies the weight vector at each step. The algorithm may be employed both in learning and in self-learning modes. The theoretical aspects of the behaviour of the algorithm are studied when the algorithm is used for the purpose of increasing the decision reliability by means of weighted voting. I...
A self-learning algorithm for biased molecular dynamics
Tribello, Gareth A.; Ceriotti, Michele; Parrinello, Michele
2010-01-01
A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences. PMID:20876135
Challenges in the Verification of Reinforcement Learning Algorithms
Van Wesel, Perry; Goodloe, Alwyn E.
2017-01-01
Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.
An Algorithm for constructing Hjelmslev planes
Hall, Joanne L.; Rao, Asha
2013-01-01
Projective Hjelmslev planes and Affine Hjelmselv planes are generalisations of projective planes and affine planes. We present an algorithm for constructing a projective Hjelmslev planes and affine Hjelsmelv planes using projective planes, affine planes and orthogonal arrays. We show that all 2-uniform projective Hjelmslev planes, and all 2-uniform affine Hjelsmelv planes can be constructed in this way. As a corollary it is shown that all 2-uniform Affine Hjelmselv planes are sub-geometries o...
An algorithm for learning real-time automata
Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.
2007-01-01
We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe
Greedy algorithms withweights for construction of partial association rules
Moshkov, Mikhail; Piliszczu, Marcin; Zielosko, Beata Marta
2009-01-01
This paper is devoted to the study of approximate algorithms for minimization of the total weight of attributes occurring in partial association rules. We consider mainly greedy algorithms with weights for construction of rules. The paper contains bounds on precision of these algorithms and bounds on the minimal weight of partial association rules based on an information obtained during the greedy algorithm run.
Greedy algorithms withweights for construction of partial association rules
Moshkov, Mikhail
2009-09-10
This paper is devoted to the study of approximate algorithms for minimization of the total weight of attributes occurring in partial association rules. We consider mainly greedy algorithms with weights for construction of rules. The paper contains bounds on precision of these algorithms and bounds on the minimal weight of partial association rules based on an information obtained during the greedy algorithm run.
Cascade Error Projection: A New Learning Algorithm
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.
Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate
Energy Technology Data Exchange (ETDEWEB)
Lv Jiancheng [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China); Yi Zhang [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)]. E-mail: zhangyi@uestc.edu.cn
2007-05-15
The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm.
A method for classification of network traffic based on C5.0 Machine Learning Algorithm
DEFF Research Database (Denmark)
Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup
2012-01-01
current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown...... and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options...
Learning Sorting Algorithms through Visualization Construction
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…
Q-learning-based adjustable fixed-phase quantum Grover search algorithm
International Nuclear Information System (INIS)
Guo Ying; Shi Wensha; Wang Yijun; Hu, Jiankun
2017-01-01
We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one. (author)
Research on machine learning framework based on random forest algorithm
Ren, Qiong; Cheng, Hui; Han, Hai
2017-03-01
With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.
Trans-algorithmic nature of learning in biological systems.
Shimansky, Yury P
2018-05-02
Learning ability is a vitally important, distinctive property of biological systems, which provides dynamic stability in non-stationary environments. Although several different types of learning have been successfully modeled using a universal computer, in general, learning cannot be described by an algorithm. In other words, algorithmic approach to describing the functioning of biological systems is not sufficient for adequate grasping of what is life. Since biosystems are parts of the physical world, one might hope that adding some physical mechanisms and principles to the concept of algorithm could provide extra possibilities for describing learning in its full generality. However, a straightforward approach to that through the so-called physical hypercomputation so far has not been successful. Here an alternative approach is proposed. Biosystems are described as achieving enumeration of possible physical compositions though random incremental modifications inflicted on them by active operating resources (AORs) in the environment. Biosystems learn through algorithmic regulation of the intensity of the above modifications according to a specific optimality criterion. From the perspective of external observers, biosystems move in the space of different algorithms driven by random modifications imposed by the environmental AORs. A particular algorithm is only a snapshot of that motion, while the motion itself is essentially trans-algorithmic. In this conceptual framework, death of unfit members of a population, for example, is viewed as a trans-algorithmic modification made in the population as a biosystem by environmental AORs. Numerous examples of AOR utilization in biosystems of different complexity, from viruses to multicellular organisms, are provided.
Improvement of the cost-benefit analysis algorithm for high-rise construction projects
Directory of Open Access Journals (Sweden)
Gafurov Andrey
2018-01-01
Full Text Available The specific nature of high-rise investment projects entailing long-term construction, high risks, etc. implies a need to improve the standard algorithm of cost-benefit analysis. An improved algorithm is described in the article. For development of the improved algorithm of cost-benefit analysis for high-rise construction projects, the following methods were used: weighted average cost of capital, dynamic cost-benefit analysis of investment projects, risk mapping, scenario analysis, sensitivity analysis of critical ratios, etc. This comprehensive approach helped to adapt the original algorithm to feasibility objectives in high-rise construction. The authors put together the algorithm of cost-benefit analysis for high-rise construction projects on the basis of risk mapping and sensitivity analysis of critical ratios. The suggested project risk management algorithms greatly expand the standard algorithm of cost-benefit analysis in investment projects, namely: the “Project analysis scenario” flowchart, improving quality and reliability of forecasting reports in investment projects; the main stages of cash flow adjustment based on risk mapping for better cost-benefit project analysis provided the broad range of risks in high-rise construction; analysis of dynamic cost-benefit values considering project sensitivity to crucial variables, improving flexibility in implementation of high-rise projects.
Improvement of the cost-benefit analysis algorithm for high-rise construction projects
Gafurov, Andrey; Skotarenko, Oksana; Plotnikov, Vladimir
2018-03-01
The specific nature of high-rise investment projects entailing long-term construction, high risks, etc. implies a need to improve the standard algorithm of cost-benefit analysis. An improved algorithm is described in the article. For development of the improved algorithm of cost-benefit analysis for high-rise construction projects, the following methods were used: weighted average cost of capital, dynamic cost-benefit analysis of investment projects, risk mapping, scenario analysis, sensitivity analysis of critical ratios, etc. This comprehensive approach helped to adapt the original algorithm to feasibility objectives in high-rise construction. The authors put together the algorithm of cost-benefit analysis for high-rise construction projects on the basis of risk mapping and sensitivity analysis of critical ratios. The suggested project risk management algorithms greatly expand the standard algorithm of cost-benefit analysis in investment projects, namely: the "Project analysis scenario" flowchart, improving quality and reliability of forecasting reports in investment projects; the main stages of cash flow adjustment based on risk mapping for better cost-benefit project analysis provided the broad range of risks in high-rise construction; analysis of dynamic cost-benefit values considering project sensitivity to crucial variables, improving flexibility in implementation of high-rise projects.
An Algorithm for Fault-Tree Construction
DEFF Research Database (Denmark)
Taylor, J. R.
1982-01-01
An algorithm for performing certain parts of the fault tree construction process is described. Its input is a flow sheet of the plant, a piping and instrumentation diagram, or a wiring diagram of the circuits, to be analysed, together with a standard library of component functional and failure...
Evolving Stochastic Learning Algorithm based on Tsallis entropic index
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.
Genetic algorithm learning in a New Keynesian macroeconomic setup.
Hommes, Cars; Makarewicz, Tomasz; Massaro, Domenico; Smits, Tom
2017-01-01
In order to understand heterogeneous behavior amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms (GA) model to replicate the results from their LtF experiment. In this GA model, individuals optimize an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the evolutionary learning model is able to replicate the three different types of behavior, i.e. convergence to steady state, stable oscillations and dampened oscillations in the treatments using one GA model. Heterogeneous behavior can thus be explained by an adaptive, anchor and trend extrapolating component and the GA model can be used to explain heterogeneous behavior in LtF experiments with different types of complexity.
Leave-two-out stability of ontology learning algorithm
International Nuclear Information System (INIS)
Wu, Jianzhang; Yu, Xiao; Zhu, Linli; Gao, Wei
2016-01-01
Ontology is a semantic analysis and calculation model, which has been applied to many subjects. Ontology similarity calculation and ontology mapping are employed as machine learning approaches. The purpose of this paper is to study the leave-two-out stability of ontology learning algorithm. Several leave-two-out stabilities are defined in ontology learning setting and the relationship among these stabilities are presented. Furthermore, the results manifested reveal that leave-two-out stability is a sufficient and necessary condition for ontology learning algorithm.
A Learning Algorithm based on High School Teaching Wisdom
Philip, Ninan Sajeeth
2010-01-01
A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces better learning curves by demanding the student to optimally dedicate their learning time on the failed examples. When used in machine learning, the algorithm is found to train a machine...
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.
Top Tagging by Deep Learning Algorithm
Akil, Ali
2015-01-01
In this report I will show the application of a deep learning algorithm on a Monte Carlo simulation sample to test its performance in tagging hadronic decays of boosted top quarks and compare what we get with the results of the application of some other algorithms.
Learning Intelligent Genetic Algorithms Using Japanese Nonograms
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…
SOL: A Library for Scalable Online Learning Algorithms
Wu, Yue; Hoi, Steven C. H.; Liu, Chenghao; Lu, Jing; Sahoo, Doyen; Yu, Nenghai
2016-01-01
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, portability, and extensibility. SOL was implemented in C++, and provided with a collection of easy-to-use command-line tools, python wrappers and library calls for users and develope...
Algorithmic learning in a random world
Vovk, Vladimir; Shafer, Glenn
2005-01-01
A new scientific monograph developing significant new algorithmic foundations in machine learning theory. Researchers and postgraduates in CS, statistics, and A.I. will find the book an authoritative and formal presentation of some of the most promising theoretical developments in machine learning.
Location-Aware Mobile Learning of Spatial Algorithms
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…
QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.
Zwartjes, Ardjan; Havinga, Paul J M; Smit, Gerard J M; Hurink, Johann L
2016-10-01
In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.
Efficient Geo-Computational Algorithms for Constructing Space-Time Prisms in Road Networks
Directory of Open Access Journals (Sweden)
Hui-Ping Chen
2016-11-01
Full Text Available The Space-time prism (STP is a key concept in time geography for analyzing human activity-travel behavior under various Space-time constraints. Most existing time-geographic studies use a straightforward algorithm to construct STPs in road networks by using two one-to-all shortest path searches. However, this straightforward algorithm can introduce considerable computational overhead, given the fact that accessible links in a STP are generally a small portion of the whole network. To address this issue, an efficient geo-computational algorithm, called NTP-A*, is proposed. The proposed NTP-A* algorithm employs the A* and branch-and-bound techniques to discard inaccessible links during two shortest path searches, and thereby improves the STP construction performance. Comprehensive computational experiments are carried out to demonstrate the computational advantage of the proposed algorithm. Several implementation techniques, including the label-correcting technique and the hybrid link-node labeling technique, are discussed and analyzed. Experimental results show that the proposed NTP-A* algorithm can significantly improve STP construction performance in large-scale road networks by a factor of 100, compared with existing algorithms.
Hinchey, Michael G. (Inventor); Margaria, Tiziana (Inventor); Rash, James L. (Inventor); Rouff, Christopher A. (Inventor); Steffen, Bernard (Inventor)
2010-01-01
Systems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required.
Human resource recommendation algorithm based on ensemble learning and Spark
Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie
2017-08-01
Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.
A Learning Algorithm for Multimodal Grammar Inference.
D'Ulizia, A; Ferri, F; Grifoni, P
2011-12-01
The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.
ALGORITHMIC CONSTRUCTION SCHEDULES IN CONDITIONS OF TIMING CONSTRAINTS
Directory of Open Access Journals (Sweden)
Alexey S. Dobrynin
2014-01-01
Full Text Available Tasks of time-schedule construction (JSSP in various fields of human activities have an important theoretical and practical significance. The main feature of these tasks is a timing requirement, describing allowed planning time periods and periods of downtime. This article describes implementation variations of the work scheduling algorithm under timing requirements for the tasks of industrial time-schedules construction, and service activities.
Internet-based Interactive Construction Management Learning System.
Sawhney, Anil; Mund, Andre; Koczenasz, Jeremy
2001-01-01
Describes a way to incorporate practical content into the construction engineering and management curricula: the Internet-based Interactive Construction Management Learning System, which uses interactive and adaptive learning environments to train students in the areas of construction methods, equipment and processes using multimedia, databases,…
QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms
Directory of Open Access Journals (Sweden)
Ardjan Zwartjes
2016-10-01
Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.
Algorithm of composing the schedule of construction and installation works
Nehaj, Rustam; Molotkov, Georgij; Rudchenko, Ivan; Grinev, Anatolij; Sekisov, Aleksandr
2017-10-01
An algorithm for scheduling works is developed, in which the priority of the work corresponds to the total weight of the subordinate works, the vertices of the graph, and it is proved that for graphs of the tree type the algorithm is optimal. An algorithm is synthesized to reduce the search for solutions when drawing up schedules of construction and installation works, allocating a subset with the optimal solution of the problem of the minimum power, which is determined by the structure of its initial data and numerical values. An algorithm for scheduling construction and installation work is developed, taking into account the schedule for the movement of brigades, which is characterized by the possibility to efficiently calculate the values of minimizing the time of work performance by the parameters of organizational and technological reliability through the use of the branch and boundary method. The program of the computational algorithm was compiled in the MatLAB-2008 program. For the initial data of the matrix, random numbers were taken, uniformly distributed in the range from 1 to 100. It takes 0.5; 2.5; 7.5; 27 minutes to solve the problem. Thus, the proposed method for estimating the lower boundary of the solution is sufficiently accurate and allows efficient solution of the minimax task of scheduling construction and installation works.
An Efficient Distributed Algorithm for Constructing Spanning Trees in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Rosana Lachowski
2015-01-01
Full Text Available Monitoring and data collection are the two main functions in wireless sensor networks (WSNs. Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.
A strategy for quantum algorithm design assisted by machine learning
International Nuclear Information System (INIS)
Bang, Jeongho; Lee, Jinhyoung; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin
2014-01-01
We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum–classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch–Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method. (paper)
A strategy for quantum algorithm design assisted by machine learning
Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung
2014-07-01
We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.
Learning from nature: Nature-inspired algorithms
DEFF Research Database (Denmark)
Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin
2016-01-01
.), genetic and evolutionary strategies, artificial immune systems etc. Well-known examples of applications include: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based......During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms...
Parallelization of TMVA Machine Learning Algorithms
Hajili, Mammad
2017-01-01
This report reflects my work on Parallelization of TMVA Machine Learning Algorithms integrated to ROOT Data Analysis Framework during summer internship at CERN. The report consists of 4 impor- tant part - data set used in training and validation, algorithms that multiprocessing applied on them, parallelization techniques and re- sults of execution time changes due to number of workers.
Exploitation of linkage learning in evolutionary algorithms
Chen, Ying-ping
2010-01-01
The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.
Relevance as a metric for evaluating machine learning algorithms
Kota Gopalakrishna, A.; Ozcelebi, T.; Liotta, A.; Lukkien, J.J.
2013-01-01
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel
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.
Niazmardi, S.; Safari, A.; Homayouni, S.
2017-09-01
Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.
Teaching learning based optimization algorithm and its engineering applications
Rao, R Venkata
2016-01-01
Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.
A distributed algorithm for machine learning
Chen, Shihong
2018-04-01
This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.
Automatic learning algorithm for the MD-logic artificial pancreas system.
Miller, Shahar; Nimri, Revital; Atlas, Eran; Grunberg, Eli A; Phillip, Moshe
2011-10-01
Applying real-time learning into an artificial pancreas system could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly. We describe a novel learning algorithm and its performance when integrated into the MD-Logic Artificial Pancreas (MDLAP) system developed by the Diabetes Technology Center, Schneider Children's Medical Center of Israel, Petah Tikva, Israel. The algorithm was designed to establish an initial patient profile using open-loop data (Initial Learning Algorithm component) and then make periodic adjustments during closed-loop operation (Runtime Learning Algorithm component). The MDLAP system, integrated with the learning algorithm, was tested in seven different experiments using the University of Virginia/Padova simulator, comprising adults, adolescents, and children. The experiments included simulations using the open-loop and closed-loop control strategy under nominal and varying insulin sensitivity conditions. The learning algorithm was automatically activated at the end of the open-loop segment and after every day of the closed-loop operation. Metabolic control parameters achieved at selected time points were compared. The percentage of time glucose levels were maintained within 70-180 mg/dL for children and adolescents significantly improved when open-loop was compared with day 6 of closed-loop control (Psignificantly reduced by approximately sevenfold (Psignificant reduction in the Low Blood Glucose Index (P<0.001). The new algorithm was effective in characterizing the patient profiles from open-loop data and in adjusting treatment to provide better glycemic control during closed-loop control in both conditions. These findings warrant corroboratory clinical trials.
Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System
Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang
2018-03-01
Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.
An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance
Grivokostopoulou, Foteini; Perikos, Isidoros; Hatzilygeroudis, Ioannis
2017-01-01
In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active…
Machine-Learning Algorithms to Code Public Health Spending Accounts.
Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David
Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.
Expansive Learning in Construction Projects - a Contradiction in terms?
DEFF Research Database (Denmark)
Klitgaard, Anne; Nissen, Søren Bülow; Beck, Frederikke
2016-01-01
This research is a preliminary study performed as part of a primary research into expansive learning in interorganizational network set up to solve a construction project. The construction industry has long had issues about productivity, which can be an indication of lack of learning. A case study...... acquisition and participation but not by expansive learning. The construction industry needs to accept that the learning generated from projects will be limited to learning by acquisition and participation. The interorganizational network cannot facilitate expansive learning while working on object......-fixed projects. Research in construction management fails to generate and document knowledge because of the limitations of case studies....
A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms
Hayden Wimmer; Loreen Powell
2014-01-01
While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Baye...
International Nuclear Information System (INIS)
Tanabe, Akira; Yamamoto, Toru; Shinfuku, Kimihiro; Nakamae, Takuji; Nishide, Fusayo.
1995-01-01
Previously a two-layered neural network model was developed to predict the relation between fissile enrichment of each fuel rod and local power distribution in a BWR fuel bundle. This model was obtained intuitively based on 33 patterns of training signals after an intensive survey of the models. Recently, a learning algorithm with forgetting was reported to simplify neural network models. It is an interesting subject what kind of model will be obtained if this algorithm is applied to the complex three-layered model which learns the same training signals. A three-layered model which is expanded to have direct connections between the 1st and the 3rd layer elements has been constructed and the learning method of normal back propagation was applied first to this model. The forgetting algorithm was then added to this learning process. The connections concerned with the 2nd layer elements disappeared and the 2nd layer has become unnecessary. It took a longer computing time by an order to learn the same training signals than the simple back propagation, but the two-layered model was obtained autonomously from the expanded three-layered model. (author)
An algorithm to construct Groebner bases for solving integration by parts relations
International Nuclear Information System (INIS)
Smirnov, Alexander V.
2006-01-01
This paper is a detailed description of an algorithm based on a generalized Buchberger algorithm for constructing Groebner-type bases associated with polynomials of shift operators. The algorithm is used to calculate Feynman integrals and has proved to be efficient in several complicated cases
Learning motor skills from algorithms to robot experiments
Kober, Jens
2014-01-01
This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters, and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation, and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which wo...
Algorithm-Dependent Generalization Bounds for Multi-Task Learning.
Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J
2017-02-01
Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.
Inverse Problems in Geodynamics Using Machine Learning Algorithms
Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.
2018-01-01
During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.
The Learning Impact of a 4-Dimensional Digital Construction Learning Environment
Chris Landorf; Stephen Ward
2017-01-01
This paper addresses a virtual environment approach to work integrated learning for students in construction-related disciplines. The virtual approach provides a safe and pedagogically rigorous environment where students can apply theoretical knowledge in a simulated real-world context. The paper describes the development of a 4-dimensional digital construction environment and associated learning activities funded by the Australian Office for Learning and Teaching. The environment was trialle...
Generalized SMO algorithm for SVM-based multitask learning.
Cai, Feng; Cherkassky, Vladimir
2012-06-01
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.
Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory
Institute of Scientific and Technical Information of China (English)
Sheng-Zhi Du; Zeng-Qiang Chen; Zhu-Zhi Yuan
2005-01-01
This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters (λ and ζ), but the relation of them is not linear. So it is hard to find a best combination of λ and ζ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.
Proposed genetic algorithms for construction site lay out
Mawdesley, Michael J.; Al-Jibouri, Saad H.S.
2003-01-01
The positioning of temporary facilities on a construction site is an area of research which has been recognised as important but which has received relatively little attention. In this paper, a genetic algorithm is proposed to solve the problem in which m facilities are to be positioned to n
An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
Directory of Open Access Journals (Sweden)
Ying Mei
2017-06-01
Full Text Available Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade. The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.
A new evolutionary algorithm with LQV learning for combinatorial problems optimization
International Nuclear Information System (INIS)
Machado, Marcelo Dornellas; Schirru, Roberto
2000-01-01
Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for combinatorial problems optimization. In this work, a new learning mode, to be used by the population-based incremental learning algorithm, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process known as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors, in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problems. Due to the fact that the reload problem is a combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)
Query construction, entropy, and generalization in neural-network models
Sollich, Peter
1994-05-01
We study query construction algorithms, which aim at improving the generalization ability of systems that learn from examples by choosing optimal, nonredundant training sets. We set up a general probabilistic framework for deriving such algorithms from the requirement of optimizing a suitable objective function; specifically, we consider the objective functions entropy (or information gain) and generalization error. For two learning scenarios, the high-low game and the linear perceptron, we evaluate the generalization performance obtained by applying the corresponding query construction algorithms and compare it to training on random examples. We find qualitative differences between the two scenarios due to the different structure of the underlying rules (nonlinear and ``noninvertible'' versus linear); in particular, for the linear perceptron, random examples lead to the same generalization ability as a sequence of queries in the limit of an infinite number of examples. We also investigate learning algorithms which are ill matched to the learning environment and find that, in this case, minimum entropy queries can in fact yield a lower generalization ability than random examples. Finally, we study the efficiency of single queries and its dependence on the learning history, i.e., on whether the previous training examples were generated randomly or by querying, and the difference between globally and locally optimal query construction.
Some chaotic behaviors in a MCA learning algorithm with a constant learning rate
International Nuclear Information System (INIS)
Lv Jiancheng; Yi Zhang
2007-01-01
Douglas's minor component analysis algorithm with a constant learning rate has both stability and chaotic dynamical behavior under some conditions. The paper explores such dynamical behavior of this algorithm. Certain stability and chaos of this algorithm are derived. Waveform plots, Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior
Learning Words through Computer-Adaptive Tool
DEFF Research Database (Denmark)
Zhang, Chun
2005-01-01
construction, I stress the design of a test theory, namely, a learning algorithm. The learning algorithm is designed under such principles that users experience both 'elaborative rehearsal’ (aspects in receptive and productive learning) and 'expanding rehearsal, (memory-based learning and repetitive act...
Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review
Directory of Open Access Journals (Sweden)
Lise Safatly
2014-01-01
reconfigurable radio frequency (RF parts, enhanced spectrum sensing algorithms, and sophisticated machine learning techniques. In this paper, we present a review of the recent advances in CR transceivers hardware design and algorithms. For the RF part, three types of antennas are presented: UWB antennas, frequency-reconfigurable/tunable antennas, and UWB antennas with reconfigurable band notches. The main challenges faced by the design of the other RF blocks are also discussed. Sophisticated spectrum sensing algorithms that overcome main sensing challenges such as model uncertainty, hardware impairments, and wideband sensing are highlighted. The cognitive engine features are discussed. Moreover, we study unsupervised classification algorithms and a reinforcement learning (RL algorithm that has been proposed to perform decision-making in CR networks.
A parallel ILP algorithm that incorporates incremental batch learning
Nuno Fonseca; Rui Camacho; Fernado Silva
2003-01-01
In this paper we tackle the problems of eciency and scala-bility faced by Inductive Logic Programming (ILP) systems. We proposethe use of parallelism to improve eciency and the use of an incrementalbatch learning to address the scalability problem. We describe a novelparallel algorithm that incorporates into ILP the method of incremen-tal batch learning. The theoretical complexity of the algorithm indicatesthat a linear speedup can be achieved.
Directory of Open Access Journals (Sweden)
Wenjing Zhao
2018-01-01
Full Text Available SGK (sequential generalization of K-means dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1 The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2 The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3 Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.
Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin
2015-10-01
The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.
Properties of healthcare teaming networks as a function of network construction algorithms.
Directory of Open Access Journals (Sweden)
Martin S Zand
Full Text Available Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year, making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast
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.
On algorithm for building of optimal α-decision trees
Alkhalid, Abdulaziz
2010-01-01
The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.
Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.
Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun
2014-01-01
The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.
Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.
Directory of Open Access Journals (Sweden)
Chihyun Park
Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.
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.
An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint
Directory of Open Access Journals (Sweden)
Ye Zhang
2014-01-01
Full Text Available 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.
The algorithm for duration acceleration of repetitive projects considering the learning effect
Chen, Hongtao; Wang, Keke; Du, Yang; Wang, Liwan
2018-03-01
Repetitive project optimization problem is common in project scheduling. Repetitive Scheduling Method (RSM) has many irreplaceable advantages in the field of repetitive projects. As the same or similar work is repeated, the proficiency of workers will be correspondingly low to high, and workers will gain experience and improve the efficiency of operations. This is learning effect. Learning effect is one of the important factors affecting the optimization results in repetitive project scheduling. This paper analyzes the influence of the learning effect on the controlling path in RSM from two aspects: one is that the learning effect changes the controlling path, the other is that the learning effect doesn't change the controlling path. This paper proposes corresponding methods to accelerate duration for different types of critical activities and proposes the algorithm for duration acceleration based on the learning effect in RSM. And the paper chooses graphical method to identity activities' types and considers the impacts of the learning effect on duration. The method meets the requirement of duration while ensuring the lowest acceleration cost. A concrete bridge construction project is given to verify the effectiveness of the method. The results of this study will help project managers understand the impacts of the learning effect on repetitive projects, and use the learning effect to optimize project scheduling.
Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants
International Nuclear Information System (INIS)
Parlos, A.G.; Atiya, Amir; Chong, K.T.
1991-01-01
A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process
Golla, Gowtham Kumar; Carlson, Jordan A; Huan, Jun; Kerr, Jacqueline; Mitchell, Tarrah; Borner, Kelsey
2016-10-01
Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Wearable accelerometers are considered as the standard for assessing physical activity in research, but do not perform well for assessing posture (i.e., sitting vs. standing), a critical component of sedentary behavior. The machine learning algorithms that we propose for assessing sedentary behavior will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. We collected two datasets, a laboratory-controlled dataset and a free-living dataset. We trained machine learning classifiers separately on each dataset and compared performance across datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\\walk. We compared a manually constructed Hidden Markov model (HMM) with an automated HMM from existing software. The manually constructed HMM gave more F1-Macro score on both datasets.
On constructing optimistic simulation algorithms for the discrete event system specification
International Nuclear Information System (INIS)
Nutaro, James J.
2008-01-01
This article describes a Time Warp simulation algorithm for discrete event models that are described in terms of the Discrete Event System Specification (DEVS). The article shows how the total state transition and total output function of a DEVS atomic model can be transformed into an event processing procedure for a logical process. A specific Time Warp algorithm is constructed around this logical process, and it is shown that the algorithm correctly simulates a DEVS coupled model that consists entirely of interacting atomic models. The simulation algorithm is presented abstractly; it is intended to provide a basis for implementing efficient and scalable parallel algorithms that correctly simulate DEVS models
Robot Competence Development by Constructive Learning
Meng, Q.; Lee, M. H.; Hinde, C. J.
This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system’s adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.
Construction of a Digital Learning Environment Based on Cloud Computing
Ding, Jihong; Xiong, Caiping; Liu, Huazhong
2015-01-01
Constructing the digital learning environment for ubiquitous learning and asynchronous distributed learning has opened up immense amounts of concrete research. However, current digital learning environments do not fully fulfill the expectations on supporting interactive group learning, shared understanding and social construction of knowledge.…
Ubiquitous mobile knowledge construction in collaborative learning environments.
Baloian, Nelson; Zurita, Gustavo
2012-01-01
Knowledge management is a critical activity for any organization. It has been said to be a differentiating factor and an important source of competitiveness if this knowledge is constructed and shared among its members, thus creating a learning organization. Knowledge construction is critical for any collaborative organizational learning environment. Nowadays workers must perform knowledge creation tasks while in motion, not just in static physical locations; therefore it is also required that knowledge construction activities be performed in ubiquitous scenarios, and supported by mobile and pervasive computational systems. These knowledge creation systems should help people in or outside organizations convert their tacit knowledge into explicit knowledge, thus supporting the knowledge construction process. Therefore in our understanding, we consider highly relevant that undergraduate university students learn about the knowledge construction process supported by mobile and ubiquitous computing. This has been a little explored issue in this field. This paper presents the design, implementation, and an evaluation of a system called MCKC for Mobile Collaborative Knowledge Construction, supporting collaborative face-to-face tacit knowledge construction and sharing in ubiquitous scenarios. The MCKC system can be used by undergraduate students to learn how to construct knowledge, allowing them anytime and anywhere to create, make explicit and share their knowledge with their co-learners, using visual metaphors, gestures and sketches to implement the human-computer interface of mobile devices (PDAs).
Assessment of various supervised learning algorithms using different performance metrics
Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.
2017-11-01
Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.
A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Alexandre Szabo
2013-01-01
Full Text Available The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first proposal, named Particle Swarm Classifier (PSClass, is a derivation of a particle swarm clustering algorithm and its architecture, as in most classifiers, is pre-defined. The second proposal, named Constructive Particle Swarm Classifier (cPSClass, uses ideas from the immune system to automatically build the swarm. A sensitivity analysis of the growing procedure of cPSClass and an investigation into a proposed pruning procedure for this algorithm are performed. The proposals were applied to a wide range of databases from the literature and the results show that they are competitive in relation to other approaches, with the advantage of having a dynamically constructed architecture.
DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.
Kalsi, Shruti; Kaur, Harleen; Chang, Victor
2017-12-05
Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.
Algorithmic fault tree construction by component-based system modeling
International Nuclear Information System (INIS)
Majdara, Aref; Wakabayashi, Toshio
2008-01-01
Computer-aided fault tree generation can be easier, faster and less vulnerable to errors than the conventional manual fault tree construction. In this paper, a new approach for algorithmic fault tree generation is presented. The method mainly consists of a component-based system modeling procedure an a trace-back algorithm for fault tree synthesis. Components, as the building blocks of systems, are modeled using function tables and state transition tables. The proposed method can be used for a wide range of systems with various kinds of components, if an inclusive component database is developed. (author)
MINING ON CAR DATABASE EMPLOYING LEARNING AND CLUSTERING ALGORITHMS
Muhammad Rukunuddin Ghalib; Shivam Vohra; Sunish Vohra; Akash Juneja
2013-01-01
In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the known learning algorithms used are Naïve Bayesian (NB) and SMO (Self-Minimal-Optimisation) .Thus the following two learning algorithms are used on a Car review database and thus a model is hence created which predicts the characteristic of a review comment after getting trained. It was found that model successfully predicted correctly about the review comm...
Warehouse sizing algorithm for edification works of construc-tion sector
Directory of Open Access Journals (Sweden)
Andres Mauricio Hualpa Zuñiga
2015-08-01
Full Text Available This article contains the development of an algorithm applied to the solution of problems of sizing of storage spaces in companies in the construction sector. This problem is justified under the degree of informality that occurs at the time of assigning storage areas, without considering parameters related to stages of construction, the characteristics of the product and the provisions of the work area. In a previous study it is identified that the degree of informality at the moment of assigning storage areas, generates poor rates of capacity utilization and delivery of incomplete orders. The design of the algorithm is supported by a comprehensive model of sizing subjected to a system of equations with variables of quantity, volume and material dimensions, to finally establish the necessary storage area. The algorithm is adapted to programming language in order to present the results in graphic language where the sizing of storage spaces is visible. These results are validated through the evaluation of storage capacity utilization and completely delivered orders for different cargo units, where improvements in these indicators are shown.
Construct validation of an interactive digital algorithm for ostomy care.
Beitz, Janice M; Gerlach, Mary A; Schafer, Vickie
2014-01-01
The purpose of this study was to evaluate construct validity for a previously face and content validated Ostomy Algorithm using digital real-life clinical scenarios. A cross-sectional, mixed-methods Web-based survey design study was conducted. Two hundred ninety-seven English-speaking RNs completed the study; participants practiced in both acute care and postacute settings, with 1 expert ostomy nurse (WOC nurse) and 2 nonexpert nurses. Following written consent, respondents answered demographic questions and completed a brief algorithm tutorial. Participants were then presented with 7 ostomy-related digital scenarios consisting of real-life photos and pertinent clinical information. Respondents used the 11 assessment components of the digital algorithm to choose management options. Participant written comments about the scenarios and the research process were collected. The mean overall percentage of correct responses was 84.23%. Mean percentage of correct responses for respondents with a self-reported basic ostomy knowledge was 87.7%; for those with a self-reported intermediate ostomy knowledge was 85.88% and those who were self-reported experts in ostomy care achieved 82.77% correct response rate. Five respondents reported having no prior ostomy care knowledge at screening and achieved an overall 45.71% correct response rate. No negative comments regarding the algorithm were recorded by participants. The new standardized Ostomy Algorithm remains the only face, content, and construct validated digital clinical decision instrument currently available. Further research on application at the bedside while tracking patient outcomes is warranted.
A fast and accurate online sequential learning algorithm for feedforward networks.
Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N
2006-11-01
In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.
International Nuclear Information System (INIS)
Kim, Dong Yun
1997-02-01
In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller
Video game for learning and metaphorization of recursive algorithms
Directory of Open Access Journals (Sweden)
Ricardo Inacio Alvares Silva
2013-09-01
Full Text Available The learning of recursive algorithms in computer programming is problematic, because its execution and resolution is not natural to the thinking way people are trained and used to since young. As with other topics in algorithms, we use metaphors to make parallels between the abstract and the concrete to help in understanding the operation of recursive algorithms. However, the classic metaphors employed in this area, such as calculating factorial recursively and Towers of Hanoi game, may just confuse more or be insufficient. In this work, we produced a computer game to assist students in computer courses in learning recursive algorithms. It was designed to have regular video game characteristics, with narrative and classical gameplay elements, commonly found in this kind of product. Aiding to education occurs through metaphorization, or in other words, through experiences provided by game situations that refer to recursive algorithms. To this end, we designed and imbued in the game four valid metaphors related to the theory, and other minor references to the subject.
Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning
Fu, QiMing
2016-01-01
To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704
SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid
Directory of Open Access Journals (Sweden)
Hossam Gabbar
2016-03-01
Full Text Available Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC with Fuzzy Model Reference Learning Controller (FMRLC. The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.
International Nuclear Information System (INIS)
Dong Yun Kim; Poong Hyun Seong; .
1997-01-01
In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)
An improved clustering algorithm based on reverse learning in intelligent transportation
Qiu, Guoqing; Kou, Qianqian; Niu, Ting
2017-05-01
With the development of artificial intelligence and data mining technology, big data has gradually entered people's field of vision. In the process of dealing with large data, clustering is an important processing method. By introducing the reverse learning method in the clustering process of PAM clustering algorithm, to further improve the limitations of one-time clustering in unsupervised clustering learning, and increase the diversity of clustering clusters, so as to improve the quality of clustering. The algorithm analysis and experimental results show that the algorithm is feasible.
Super-resolution reconstruction of MR image with a novel residual learning network algorithm
Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu
2018-04-01
Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.
Tatjewski, Marcin; Kierczak, Marcin; Plewczynski, Dariusz
2017-01-01
Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.
Lessons Learned from Past and Ongoing Construction Projects
International Nuclear Information System (INIS)
Tabatabai, Omid
2011-01-01
Full text of publication follows: The nuclear industry in the U.S. faced many construction quality and design issues in the 1970's and 1980's. In 1984, the NRC issued NUREG-1055, 'Improving Quality and the Assurance of Quality in the Design and Construction of Nuclear Power Plants,' to document the lessons learned from nuclear power plant (NPP) construction in the U.S. In recent years, several countries have begun either planning for or actually constructing new NPPs. For instance, in the U.S., the nuclear industry has submitted several combined license and design certification applications to the NRC for licensing reviews and approval to build 30+ new NPP units. Latest construction experience from countries that are currently building new NPPs indicate that these countries are dealing with challenges that are similar to those issues that caused major quality assurance problems, delays, or even termination of several projects in U.S. in the 70's and 80's. The U.S. NRC is pro-actively taking measures to improve its regulatory programs as well as construction oversight activities before new NPPs construction begin in the U.S. In late 2007, the U.S. NRC's Office of New Reactors established a construction experience program (ConE) to obtain and evaluate construction and operating experience events and to identify the lessons learned from these events. In March 2009, the NRC published an Office Instruction to provide a process for incorporating the lessons learned and insights from the design, construction, and operation of the international and domestic NPPs into the licensing reviews, inspections, and construction of new reactors in the U.S. Additionally, the ConE program staff developed a Web-enabled database to store, manage, and make construction experience information available to all NRC technical reviewers as well as inspectors. Because this database contains information from other countries' regulators that are considered
Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.
Braithwaite, Scott R; Giraud-Carrier, Christophe; West, Josh; Barnes, Michael D; Hanson, Carl Lee
2016-05-16
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. Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. 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%). 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.
A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems
Directory of Open Access Journals (Sweden)
Ke Niu
2015-01-01
Full Text Available In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.
Comparison of machine learning algorithms for detecting coral reef
Directory of Open Access Journals (Sweden)
Eduardo Tusa
2014-09-01
Full Text Available (Received: 2014/07/31 - Accepted: 2014/09/23This work focuses on developing a fast coral reef detector, which is used for an autonomous underwater vehicle, AUV. A fast detection secures the AUV stabilization respect to an area of reef as fast as possible, and prevents devastating collisions. We use the algorithm of Purser et al. (2009 because of its precision. This detector has two parts: feature extraction that uses Gabor Wavelet filters, and feature classification that uses machine learning based on Neural Networks. Due to the extensive time of the Neural Networks, we exchange for a classification algorithm based on Decision Trees. We use a database of 621 images of coral reef in Belize (110 images for training and 511 images for testing. We implement the bank of Gabor Wavelets filters using C++ and the OpenCV library. We compare the accuracy and running time of 9 machine learning algorithms, whose result was the selection of the Decision Trees algorithm. Our coral detector performs 70ms of running time in comparison to 22s executed by the algorithm of Purser et al. (2009.
Outdoor Illegal Construction Identification Algorithm Based on 3D Point Cloud Segmentation
An, Lu; Guo, Baolong
2018-03-01
Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the illegal buildings, which could address the problem above effectively. This paper proposes an outdoor illegal construction identification algorithm based on 3D point cloud segmentation. Initially, in order to save memory space and reduce processing time, a lossless point cloud compression method based on minimum spanning tree is proposed. Then, a ground point removing method based on the multi-scale filtering is introduced to increase accuracy. Finally, building clusters on the ground can be obtained using a region growing method, as a result, the illegal construction can be marked. The effectiveness of the proposed algorithm is verified using a publicly data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS).
Active-constructive-interactive: a conceptual framework for differentiating learning activities.
Chi, Michelene T H
2009-01-01
Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. The framework generates a testable hypothesis for learning: that interactive activities are most likely to be better than constructive activities, which in turn might be better than active activities, which are better than being passive. Studies from the literature are cited to provide evidence in support of this hypothesis. Moreover, postulating underlying learning processes allows us to interpret evidence in the literature more accurately. Specifying distinct overt activities for active, constructive, and interactive also offers suggestions for how learning activities can be coded and how each kind of activity might be elicited. Copyright © 2009 Cognitive Science Society, Inc.
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.
Genetic algorithm enhanced by machine learning in dynamic aperture optimization
Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert
2018-05-01
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
Hyper-parameter tuning of a decision tree induction algorithm
Mantovani, R.G.; Horváth, T.; Cerri, R.; Vanschoren, J.; de Carvalho, A.C.P.L.F.
2017-01-01
Supervised classification is the most studied task in Machine Learning. Among the many algorithms used in such task, Decision Tree algorithms are a popular choice, since they are robust and efficient to construct. Moreover, they have the advantage of producing comprehensible models and satisfactory
Algorithms and programs for consequence diagram and fault tree construction
International Nuclear Information System (INIS)
Hollo, E.; Taylor, J.R.
1976-12-01
A presentation of algorithms and programs for consequence diagram and sequential fault tree construction that are intended for reliability and disturbance analysis of large systems. The system to be analyzed must be given as a block diagram formed by mini fault trees of individual system components. The programs were written in LISP programming language and run on a PDP8 computer with 8k words of storage. A description is given of the methods used and of the program construction and working. (author)
Ballet, Stéphane; Bonnecaze, Alexis; Tukumuli, Mila
2013-01-01
International audience; We indicate a strategy in order to construct bilinear multiplication algorithms of type Chudnovsky in large extensions of any finite field. In particular, using the symmetric version of the generalization of Randriambololona specialized on the elliptic curves, we show that it is possible to construct such algorithms with low bilinear complexity. More precisely, if we only consider the Chudnovsky-type algorithms of type symmetric elliptic, we show that the symmetric bil...
Efficacy-Information for Implementing Learning in Construction
Nesan, L. Jawahar
2004-01-01
This paper, while emphasizing the linkage between the concepts of "empowerment" and "learning", argues that empowerment provides the most appropriate base to effectively implement learning in construction organizations. It is argued that "efficacy-information", being a derivative of empowerment, helps influence…
Learning Markov Decision Processes for Model Checking
DEFF Research Database (Denmark)
Mao, Hua; Chen, Yingke; Jaeger, Manfred
2012-01-01
. The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation......Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm...... on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system...
Directory of Open Access Journals (Sweden)
Valpeters Marina
2018-01-01
Full Text Available The number of experts who realize the importance of big data continues to increase in various fields of the economy. Experts begin to use big data more frequently for the solution of their specific objectives. One of the probable big data tasks in the construction industry is the determination of the probability of contract execution at a stage of its establishment. The contract holder cannot guarantee execution of the contract. Therefore it leads to a lot of risks for the customer. This article is devoted to the applicability of machine learning methods to the task of determination of the probability of a successful contract execution. Authors try to reveal the factors influencing the possibility of contract default and then try to define the following corrective actions for a customer. In the problem analysis, authors used the linear and non-linear algorithms, feature extraction, feature transformation and feature selection. The results of investigation include the prognostic models with a predictive force based on the machine learning algorithms such as logistic regression, decision tree, randomize forest. Authors have validated models on available historical data. The developed models have the potential for practical use in the construction organizations while making new contracts.
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.
Tools and Algorithms for the Construction and Analysis of Systems
DEFF Research Database (Denmark)
This book constitutes the refereed proceedings of the 10th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2004, held in Barcelona, Spain in March/April 2004. The 37 revised full papers and 6 revised tool demonstration papers presented were car...
Learning-based meta-algorithm for MRI brain extraction.
Shi, Feng; Wang, Li; Gilmore, John H; Lin, Weili; Shen, Dinggang
2011-01-01
Multiple-segmentation-and-fusion method has been widely used for brain extraction, tissue segmentation, and region of interest (ROI) localization. However, such studies are hindered in practice by their computational complexity, mainly coming from the steps of template selection and template-to-subject nonlinear registration. In this study, we address these two issues and propose a novel learning-based meta-algorithm for MRI brain extraction. Specifically, we first use exemplars to represent the entire template library, and assign the most similar exemplar to the test subject. Second, a meta-algorithm combining two existing brain extraction algorithms (BET and BSE) is proposed to conduct multiple extractions directly on test subject. Effective parameter settings for the meta-algorithm are learned from the training data and propagated to subject through exemplars. We further develop a level-set based fusion method to combine multiple candidate extractions together with a closed smooth surface, for obtaining the final result. Experimental results show that, with only a small portion of subjects for training, the proposed method is able to produce more accurate and robust brain extraction results, at Jaccard Index of 0.956 +/- 0.010 on total 340 subjects under 6-fold cross validation, compared to those by the BET and BSE even using their best parameter combinations.
A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks
Directory of Open Access Journals (Sweden)
Sho Fukuda
2014-12-01
Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks
Directory of Open Access Journals (Sweden)
Vivek Patel
2012-08-01
Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.
Two Algorithms for Learning the Parameters of Stochastic Context-Free Grammars
National Research Council Canada - National Science Library
Heeringa, Brent; Oates, Tim
2001-01-01
.... Most algorithms for learning them require storage and repeated processing of a sentence corpus. The memory and computational demands of such algorithms are illsuited for embedded agents such as a mobile robot...
An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration
Directory of Open Access Journals (Sweden)
Wenping Ma
2014-01-01
Full Text Available We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED. Differential evolution (DE is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.
Dynamics of the evolution of learning algorithms by selection
International Nuclear Information System (INIS)
Neirotti, Juan Pablo; Caticha, Nestor
2003-01-01
We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results
Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Richard Lamb
2015-09-01
Full Text Available Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a “virtual” student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning. Comparison of the STAC-M and the STAC-M with inclusion of the Multiobjective Evolutionary Algorithm shows greater success in solving the Piagetian science-tasks post cognitive retraining with the Multiobjective Evolutionary Algorithm. This illustrates the potential uses of cognitive and neuropsychological computational modeling in educational research. The authors also outline the limitations and assumptions of computational modeling.
Learning resistance mutation pathways of HIV
Ramon, Jan; Dubrovskaya, Snezhana; Blockeel, Hendrik
2007-01-01
We propose a novel machine learning algorithm for learning mutation pathways of viruses from a population of viral DNA strands. More specifically, given a number of sequences, the algorithm constructs a phylogenetic tree that expresses the ancestry of the sequences, and at the same time builds a model describing dependencies between mutations that is consistent with the data as well as the phylogenetic tree. Our approach extends existing approaches for phylogenetic tree construction by not as...
Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2012-01-01
The paper is devoted to the study of a greedy algorithm for construction of approximate tests (super-reducts) This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions For a given
Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study
Directory of Open Access Journals (Sweden)
I. G. Damousis
2012-01-01
Full Text Available We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs, Artificial Neural Networks (ANNs, Fuzzy Expert Systems (FESs, and Support Vector Machines (SVMs. The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature.
International Nuclear Information System (INIS)
Park, Gee Yong; Seong, Poong Hyun
1994-01-01
In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection
Model and Algorithm for Substantiating Solutions for Organization of High-Rise Construction Project
Directory of Open Access Journals (Sweden)
Anisimov Vladimir
2018-01-01
Full Text Available In the paper the models and the algorithm for the optimal plan formation for the organization of the material and logistical processes of the high-rise construction project and their financial support are developed. The model is based on the representation of the optimization procedure in the form of a non-linear problem of discrete programming, which consists in minimizing the execution time of a set of interrelated works by a limited number of partially interchangeable performers while limiting the total cost of performing the work. The proposed model and algorithm are the basis for creating specific organization management methodologies for the high-rise construction project.
Model and Algorithm for Substantiating Solutions for Organization of High-Rise Construction Project
Anisimov, Vladimir; Anisimov, Evgeniy; Chernysh, Anatoliy
2018-03-01
In the paper the models and the algorithm for the optimal plan formation for the organization of the material and logistical processes of the high-rise construction project and their financial support are developed. The model is based on the representation of the optimization procedure in the form of a non-linear problem of discrete programming, which consists in minimizing the execution time of a set of interrelated works by a limited number of partially interchangeable performers while limiting the total cost of performing the work. The proposed model and algorithm are the basis for creating specific organization management methodologies for the high-rise construction project.
Denoising of gravitational wave signals via dictionary learning algorithms
Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.
2016-12-01
Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.
Individual Learning in Construction Projects: Professions and their Approaches
Directory of Open Access Journals (Sweden)
Ingeborg Wasif
2010-10-01
Full Text Available New materials, use of sophisticated technologies and increased customer demands, in combination with growing competition among construction companies, have led to a high organizational boundaries. The results indicate that personal networks are the most common source of learning for all professions. While clients, architects, and designers also engage in reading and attending courses, site managers and workers are less engaged in these activities. Experimenting and organizing for learning appear to be underutilized strategies by all professions. This leads to the conclusion that attempts to increase learning have to address the differences in learning behaviours of the various groups. Further, focus on experimenting and organizing for learning is a possibility to change the learning behaviour from learning as a consequence of problems to learning for future improvement.degree of specialization. For successful integration of the different professional specialists, there is a need for shared learning between project co-workers. Based on twenty eight interviews in six different Swedish construction projects, this paper illustrates strategies for individual and shared learning, among different actors and across various
Component Pin Recognition Using Algorithms Based on Machine Learning
Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang
2018-04-01
The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.
Huang, De-tian; Huang, Wei-qin; Huang, Hui; Zheng, Li-xin
2017-11-01
To make use of the prior knowledge of the image more effectively and restore more details of the edges and structures, a novel sparse coding objective function is proposed by applying the principle of the non-local similarity and manifold learning on the basis of super-resolution algorithm via sparse representation. Firstly, the non-local similarity regularization term is constructed by using the similar image patches to preserve the edge information. Then, the manifold learning regularization term is constructed by utilizing the locally linear embedding approach to enhance the structural information. The experimental results validate that the proposed algorithm has a significant improvement compared with several super-resolution algorithms in terms of the subjective visual effect and objective evaluation indices.
Alignment of Custom Standards by Machine Learning Algorithms
Directory of Open Access Journals (Sweden)
Adela Sirbu
2010-09-01
Full Text Available Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.
An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules
Directory of Open Access Journals (Sweden)
Antonio
2012-04-01
Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.
International Nuclear Information System (INIS)
Kim, Dong Yun; Seong, Poong Hyun
1996-01-01
In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller
Creating Engaging Online Learning Material with the JSAV JavaScript Algorithm Visualization Library
Karavirta, Ville; Shaffer, Clifford A.
2016-01-01
Data Structures and Algorithms are a central part of Computer Science. Due to their abstract and dynamic nature, they are a difficult topic to learn for many students. To alleviate these learning difficulties, instructors have turned to algorithm visualizations (AV) and AV systems. Research has shown that especially engaging AVs can have an impact…
Azad, Mohammad
2012-12-14
The paper is devoted to the study of a greedy algorithm for construction of approximate tests (super-reducts) This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions For a given row, we should find a decision from the set attached to this row We consider bounds on the precision of this algorithm relative to the cardinality of tests.
Algorithm Building and Learning Programming Languages Using a New Educational Paradigm
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.
Innovation in Construction: Learning Processes in implementing new Technologies
DEFF Research Database (Denmark)
Clausen, Lennie
1999-01-01
The article is concerned with the question: How do construction firms implement new technology on construction projects? A model of the implementation process is presented based on a review of the construction innovation literature, innovation theory, and organisational learning theories....
Using machine learning algorithms to guide rehabilitation planning for home care clients.
Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul
2007-12-20
Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.
Mind the Gaps: Controversies about Algorithms, Learning and Trendy Knowledge
Argenton, Gerald
2017-01-01
This article critically explores the ways by which the Web could become a more learning-oriented medium in the age of, but also in spite of, the newly bred algorithmic cultures. The social dimension of algorithms is reported in literature as being a socio-technological entanglement that has a powerful influence on users' practices and their lived…
Recommending Learning Activities in Social Network Using Data Mining Algorithms
Mahnane, Lamia
2017-01-01
In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…
MACHINE LEARNING METHODS IN DIGITAL AGRICULTURE: ALGORITHMS AND CASES
Directory of Open Access Journals (Sweden)
Aleksandr Vasilyevich Koshkarov
2018-05-01
Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.
From the social learning theory to a social learning algorithm for global optimization
Gong, Yue-Jiao; Zhang, Jun; Li, Yun
2014-01-01
Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization...
Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis
Directory of Open Access Journals (Sweden)
Charles Frank
2018-03-01
Full Text Available Smoking has been proven to negatively affect health in a multitude of ways. As of 2009, smoking has been considered the leading cause of preventable morbidity and mortality in the United States, continuing to plague the country’s overall health. This study aims to investigate the viability and effectiveness of some machine learning algorithms for predicting the smoking status of patients based on their blood tests and vital readings results. The analysis of this study is divided into two parts: In part 1, we use One-way ANOVA analysis with SAS tool to show the statistically significant difference in blood test readings between smokers and non-smokers. The results show that the difference in INR, which measures the effectiveness of anticoagulants, was significant in favor of non-smokers which further confirms the health risks associated with smoking. In part 2, we use five machine learning algorithms: Naïve Bayes, MLP, Logistic regression classifier, J48 and Decision Table to predict the smoking status of patients. To compare the effectiveness of these algorithms we use: Precision, Recall, F-measure and Accuracy measures. The results show that the Logistic algorithm outperformed the four other algorithms with Precision, Recall, F-Measure, and Accuracy of 83%, 83.4%, 83.2%, 83.44%, respectively.
Directory of Open Access Journals (Sweden)
Andrej Bugajev
2018-01-01
Full Text Available In this article, the modelling of the judicial conflict-resolution process is considered from a construction investor’s point of view. Such modelling is important for improving the risk management for construction investors and supporting sustainable city development by supporting the development of rules regulating the construction process. Thus, this raises the problem of evaluation of different decisions and selection of the optimal one followed by distribution extraction. First, the example of such a process is analysed and schematically represented. Then, it is formalised as a graph, which is described in the form of a decision graph with cycles. We use some natural problem properties and provide the algorithm to convert this graph into a tree. Then, we propose the algorithm to evaluate profits for different scenarios with estimation of time, which is done by integration of an average daily costs function. Afterwards, the optimisation problem is solved and the optimal investor strategy is obtained—this allows one to extract the construction project profit distribution, which can be used for further analysis by standard risk (and other important information-evaluation techniques. The overall algorithm complexity is analysed, the computational experiment is performed and conclusions are formulated.
Schwarz, Daniel; Štourač, Petr; Komenda, Martin; Harazim, Hana; Kosinová, Martina; Gregor, Jakub; Hůlek, Richard; Smékalová, Olga; Křikava, Ivo; Štoudek, Roman; Dušek, Ladislav
2013-07-08
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. 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. 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. In total, 25 interactive algorithms in the Czech and English languages have been developed and published on the AKUTNE.CZ education portal to allow the users to test and improve their knowledge and skills in the field of acute medicine. In the feedback survey, 62 participants completed the online questionnaire (13.5%) from the total 460 addressed
Kamatani, Takashi; Fukunaga, Koichi; Miyata, Kaede; Shirasaki, Yoshitaka; Tanaka, Junji; Baba, Rie; Matsusaka, Masako; Kamatani, Naoyuki; Moro, Kazuyo; Betsuyaku, Tomoko; Uemura, Sotaro
2017-12-04
For single-cell experiments, it is important to accurately count the number of viable cells in a nanoliter well. We used a deep learning-based convolutional neural network (CNN) on a large amount of digital data obtained as microscopic images. The training set consisted of 103 019 samples, each representing a microscopic grayscale image. After extensive training, the CNN was able to classify the samples into four categories, i.e., 0, 1, 2, and more than 2 cells per well, with an accuracy of 98.3% when compared to determination by two trained technicians. By analyzing the samples for which judgments were discordant, we found that the judgment by technicians was relatively correct although cell counting was often difficult by the images of discordant samples. Based on the results, the system was further enhanced by introducing a new algorithm in which the highest outputs from CNN were used, increasing the accuracy to higher than 99%. Our system was able to classify the data even from wells with a different shape. No other tested machine learning algorithm showed a performance higher than that of our system. The presented CNN system is expected to be useful for various single-cell experiments, and for high-throughput and high-content screening.
Construction of Hamiltonians by supervised learning of energy and entanglement spectra
Fujita, Hiroyuki; Nakagawa, Yuya O.; Sugiura, Sho; Oshikawa, Masaki
2018-02-01
Correlated many-body problems ubiquitously appear in various fields of physics such as condensed matter, nuclear, and statistical physics. However, due to the interplay of the large number of degrees of freedom, it is generically impossible to treat these problems from first principles. Thus the construction of a proper model, namely, effective Hamiltonian, is essential. Here, we propose a simple supervised learning algorithm for constructing Hamiltonians from given energy or entanglement spectra. We apply the proposed scheme to the Hubbard model at the half-filling, and compare the obtained effective low-energy spin model with several analytic results based on the high-order perturbation theory, which have been inconsistent with each other. We also show that our approach can be used to construct the entanglement Hamiltonian of a quantum many-body state from its entanglement spectrum as well. We exemplify this using the ground states of the S =1 /2 two-leg Heisenberg ladders. We observe a qualitative difference between the entanglement Hamiltonians of the two phases (the Haldane and the rung singlet phase) of the model due to the different origin of the entanglement. In the Haldane phase, we find that the entanglement Hamiltonian is nonlocal by nature, and the locality can be restored by introducing the anisotropy and turning the ground state into the large-D phase. Possible applications to the model construction from experimental data and to various problems of strongly correlated systems are discussed.
Identification of chaotic systems by neural network with hybrid learning algorithm
International Nuclear Information System (INIS)
Pan, S.-T.; Lai, C.-C.
2008-01-01
Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods
Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model
Hamam, Alwaleed A.
2017-03-13
Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it\\'s time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.
Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model
Hamam, Alwaleed A.; Khan, Ayaz H.
2017-01-01
Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it's time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.
Behavioral Modeling for Mental Health using Machine Learning Algorithms.
Srividya, M; Mohanavalli, S; Bhalaji, N
2018-04-03
Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.
E-Learning System Using Segmentation-Based MR Technique for Learning Circuit Construction
Takemura, Atsushi
2016-01-01
This paper proposes a novel e-Learning system using the mixed reality (MR) technique for technical experiments involving the construction of electronic circuits. The proposed system comprises experimenters' mobile computers and a remote analysis system. When constructing circuits, each learner uses a mobile computer to transmit image data from the…
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2013-01-01
Full Text Available Teaching-Learning-based optimization (TLBO is a recently proposed population based algorithm, which simulates the teaching-learning process of the class room. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. In this paper, the effect of elitism on the performance of the TLBO algorithm is investigated while solving unconstrained benchmark problems. The effects of common control parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. A statistical test is also performed to investigate the results obtained using different algorithms. The results have proved the effectiveness of the proposed elitist TLBO algorithm.
Reinforcement Learning for Online Control of Evolutionary Algorithms
Eiben, A.; Horvath, Mark; Kowalczyk, Wojtek; Schut, Martijn
2007-01-01
The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We
A globally convergent MC algorithm with an adaptive learning rate.
Peng, Dezhong; Yi, Zhang; Xiang, Yong; Zhang, Haixian
2012-02-01
This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.
Directory of Open Access Journals (Sweden)
Sindi Z. Mthembu
2013-06-01
Full Text Available Background: Practices in higher education have been criticised for not developing and preparing students for the expertise required in real environments. Literature reports that educational programmes tend to favour knowledge conformation rather than knowledge construction; however, community service learning (CSL is a powerful pedagogical strategy that encourages students to make meaningful connections between the content in the classroom and real-life experiences as manifested by the communities. Through CSL, learning is achieved by the active construction of knowledge supported by multiple perspectives within meaningful real contexts, and the social interactions amongst students are seen to play a critical role in the processes of learning and cognition. This article reflects facilitators’ perspective of the knowledge construction process as used with students doing community service learning in basic nursing programmes. Objectives: The aim of this article was to conceptualise the phenomenon of knowledge construction and thereby provide educators with a shared meaning and common understanding, and to analyse the interaction strategies utilised by nurse educators in the process of knowledge construction in community service-learning programmes in basic nursing education. Method: A qualitative research approach based on a grounded theory research design was used in this article. Two nursing education institutions were purposively selected. Structured interviews were conducted with 16 participants. Results: The results revealed that the knowledge construction in community service-learning programmes is conceptualised as having specific determinants, including the use of authentic health-related problems, academic coaching through scaffolding, academic discourse-dialogue, interactive learning in communities of learners, active learning, continuous reflection as well as collaborative and inquiry-based learning. Upon completion of an experience
Mthembu, Sindi Z; Mtshali, Fikile G
2013-01-01
Practices in higher education have been criticised for not developing and preparing students for the expertise required in real environments. Literature reports that educational programmes tend to favour knowledge conformation rather than knowledge construction; however, community service learning (CSL) is a powerful pedagogical strategy that encourages students to make meaningful connections between the content in the classroom and real-life experiences as manifested by the communities. Through CSL, learning is achieved by the active construction of knowledge supported by multiple perspectives within meaningful real contexts, and the social interactions amongst students are seen to play a critical role in the processes of learning and cognition. This article reflects facilitators’ perspective of the knowledge construction process as used with students doing community service learning in basic nursing programmes. The aim of this article was to conceptualise the phenomenon of knowledge construction and thereby provide educators with a shared meaning and common understanding, and to analyse the interaction strategies utilised by nurse educators in the process of knowledge construction in community service-learning programmes in basic nursing education. A qualitative research approach based on a grounded theory research design was used in this article. Two nursing education institutions were purposively selected. Structured interviews were conducted with 16 participants. The results revealed that the knowledge construction in community service-learning programmes is conceptualised as having specific determinants, including the use of authentic health-related problems, academic coaching through scaffolding, academic discourse-dialogue, interactive learning in communities of learners, active learning, continuous reflection as well as collaborative and inquiry-based learning. Upon completion of an experience, students create and test generated knowledge in different
Alabdulmohsin, Ibrahim Mansour
2017-05-07
Active learning is a subfield of machine learning that has been successfully used in many applications. One of the main branches of active learning is query synthe- sis, where the learning agent constructs artificial queries from scratch in order to reveal sensitive information about the underlying decision boundary. It has found applications in areas, such as adversarial reverse engineering, automated science, and computational chemistry. Nevertheless, the existing literature on membership query synthesis has, generally, focused on finite concept classes or toy problems, with a limited extension to real-world applications. In this thesis, I develop two spectral algorithms for learning halfspaces via query synthesis. The first algorithm is a maximum-determinant convex optimization method while the second algorithm is a Markovian method that relies on Khachiyan’s classical update formulas for solving linear programs. The general theme of these methods is to construct an ellipsoidal approximation of the version space and to synthesize queries, afterward, via spectral decomposition. Moreover, I also describe how these algorithms can be extended to other settings as well, such as pool-based active learning. Having demonstrated that halfspaces can be learned quite efficiently via query synthesis, the second part of this thesis proposes strategies for mitigating the risk of reverse engineering in adversarial environments. One approach that can be used to render query synthesis algorithms ineffective is to implement a randomized response. In this thesis, I propose a semidefinite program (SDP) for learning a distribution of classifiers, subject to the constraint that any individual classifier picked at random from this distributions provides reliable predictions with a high probability. This algorithm is, then, justified both theoretically and empirically. A second approach is to use a non-parametric classification method, such as similarity-based classification. In this
International Nuclear Information System (INIS)
Muda, Zakaria Che; Thiruchelvam, Sivadass; Mustapha, Kamal Nasharuddin; Omar, Rohayu Che; Usman, Fathoni; Alam, Md Ashrafu
2013-01-01
Optimization of transmission tower structures is traditionally based on either optimization of members sizes with fixed topographical shape or based on structural analysis modelling strategies without taking cognizance of fabrication and constructability issue facing the contractors . This paper look into an integrated optimum design approach strategies whereby size, shape and topology are combined together with the fabrication issues in the construction of the transmission tower. The topographical algorithm is based on changing the inclination degree of the legs of the tower at first with optimum individual members sizing and later rationalized member sizes are performed through member groupings for the ease fabrication and construction of the transmission tower. The optimum weight using topographical algorithm obtained for the transmission tower is 10,924 kg for singular members and 18,430 kg for element grouping at 10° inclination angle.
Directory of Open Access Journals (Sweden)
C. Boldisor
2009-12-01
Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.
Extracting quantum dynamics from genetic learning algorithms through principal control analysis
International Nuclear Information System (INIS)
White, J L; Pearson, B J; Bucksbaum, P H
2004-01-01
Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)
QUEST : Eliminating online supervised learning for efficient classification algorithms
Zwartjes, Ardjan; Havinga, Paul J.M.; Smit, Gerard J.M.; Hurink, Johann L.
2016-01-01
In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting
Sequence-based prediction of protein protein interaction using a deep-learning algorithm.
Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng
2017-05-25
Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.
Energy Technology Data Exchange (ETDEWEB)
Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)
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.
International Nuclear Information System (INIS)
Bornholdt, S.
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
Beyond the "c" and the "x": Learning with algorithms in massive open online courses (MOOCs)
Knox, Jeremy
2018-02-01
This article examines how algorithms are shaping student learning in massive open online courses (MOOCs). Following the dramatic rise of MOOC platform organisations in 2012, over 4,500 MOOCs have been offered to date, in increasingly diverse languages, and with a growing requirement for fees. However, discussions of learning in MOOCs remain polarised around the "xMOOC" and "cMOOC" designations. In this narrative, the more recent extended or platform MOOC ("xMOOC") adopts a broadcast pedagogy, assuming a direct transmission of information to its largely passive audience (i.e. a teacher-centred approach), while the slightly older connectivist model ("cMOOC") offers only a simplistic reversal of the hierarchy, posing students as highly motivated, self-directed and collaborative learners (i.e. a learner-centred approach). The online nature of both models generates data (e.g. on how many times a particular resource was viewed, or the ways in which participants communicated with each other) which MOOC providers use for analysis, albeit only after these data have been selectively processed. Central to many learning analytics approaches is the desire to predict students' future behaviour. Educators need to be aware that MOOC learning is not just about teachers and students, but that it also involves algorithms: instructions which perform automated calculations on data. Education is becoming embroiled in an "algorithmic culture" that defines educational roles, forecasts attainment, and influences pedagogy. Established theories of learning appear wholly inadequate in addressing the agential role of algorithms in the educational domain of the MOOC. This article identifies and examines four key areas where algorithms influence the activities of the MOOC: (1) data capture and discrimination; (2) calculated learners; (3) feedback and entanglement; and (4) learning with algorithms. The article concludes with a call for further research in these areas to surface a critical
Directory of Open Access Journals (Sweden)
OMER MAHMOUD
2007-08-01
Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.
Algorithm of constructing hybrid effective modules for elastic isotropic composites
Svetashkov, A. A.; Miciński, J.; Kupriyanov, N. A.; Barashkov, V. N.; Lushnikov, A. V.
2017-02-01
The algorithm of constructing of new effective elastic characteristics of two-component composites based on the superposition of the models of Reiss and Voigt, Hashin and Strikman, as well as models of the geometric average for effective modules. These effective characteristics are inside forks Voigt and Reiss. Additionally, the calculations of the stress-strain state of composite structures with new effective characteristics give more accurate prediction than classical models do.
The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents
Directory of Open Access Journals (Sweden)
Ziad Salem
2014-12-01
Full Text Available Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs. Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents in the arena (environment”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2014-01-01
Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.
Directory of Open Access Journals (Sweden)
Arazi Idrus
2017-12-01
Full Text Available In this paper, we present our work-in-progress of a proposed framework for automated negotiation in the construction domain. The proposed framework enables software agents to conduct negotiations and autonomously make value-based decisions. The framework consists of three main components which are, solution generator algorithm, negotiation algorithm, and conflict resolution algorithm. This paper extends the discussion on the solution generator algorithm that enables software agents to generate solutions and rank them from 1st to nth solution for the negotiation stage of the operation. The solution generator algorithm consists of three steps which are, review solutions, rank solutions, and form ranked solutions. For validation purpose, we present a scenario that utilizes the proposed algorithm to rank solutions. The validation shows that the algorithm is promising, however, it also highlights the conflict between different parties that needs further negotiation action.
Spike sorting based upon machine learning algorithms (SOMA).
Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F
2007-02-15
We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.
A new algorithm for construction of coarse-grained sites of large biomolecules.
Li, Min; Zhang, John Z H; Xia, Fei
2016-04-05
The development of coarse-grained (CG) models for large biomolecules remains a challenge in multiscale simulations, including a rigorous definition of CG representations for them. In this work, we proposed a new stepwise optimization imposed with the boundary-constraint (SOBC) algorithm to construct the CG sites of large biomolecules, based on the s cheme of essential dynamics CG. By means of SOBC, we can rigorously derive the CG representations of biomolecules with less computational cost. The SOBC is particularly efficient for the CG definition of large systems with thousands of residues. The resulted CG sites can be parameterized as a CG model using the normal mode analysis based fluctuation matching method. Through normal mode analysis, the obtained modes of CG model can accurately reflect the functionally related slow motions of biomolecules. The SOBC algorithm can be used for the construction of CG sites of large biomolecules such as F-actin and for the study of mechanical properties of biomaterials. © 2015 Wiley Periodicals, Inc.
International Nuclear Information System (INIS)
Liu, Hui; Tian, Hong-qi; Li, Yan-fei
2015-01-01
Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions
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.
Energy Technology Data Exchange (ETDEWEB)
Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering
2017-10-04
Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO_{2}, NO_{x}, VOC, PM_{10}) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.
Directory of Open Access Journals (Sweden)
P. Hashemi
2018-01-01
Full Text Available Construction sites are accident-prone locations and therefore safety management plays an im-portant role in these workplaces. This study presents an adaptive algorithm for performance as-sessment of project management with respect to resilience engineering and job security in a large construction site. The required data are collected using questionnaires in a large construction site. The presented algorithm is composed of radial basis function (RBF, artificial neural networks multi-layer perceptron (ANN-MLP, and statistical tests. The results indicate that preparedness, fault-tolerance, and flexibility are the most effective factors on overall efficiency. Moreover, job security and resilience engineering have similar statistical impacts on overall system efficiency. The results are verified and validated by the proposed algorithm.
Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?
Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W
2018-03-01
The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.
FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks.
Zhang, Zhen; Zhao, Dongbin; Gao, Junwei; Wang, Dongqing; Dai, Yujie
2017-06-01
In this paper, we propose a multiagent reinforcement learning algorithm dealing with fully cooperative tasks. The algorithm is called frequency of the maximum reward Q-learning (FMRQ). FMRQ aims to achieve one of the optimal Nash equilibria so as to optimize the performance index in multiagent systems. The frequency of obtaining the highest global immediate reward instead of immediate reward is used as the reinforcement signal. With FMRQ each agent does not need the observation of the other agents' actions and only shares its state and reward at each step. We validate FMRQ through case studies of repeated games: four cases of two-player two-action and one case of three-player two-action. It is demonstrated that FMRQ can converge to one of the optimal Nash equilibria in these cases. Moreover, comparison experiments on tasks with multiple states and finite steps are conducted. One is box-pushing and the other one is distributed sensor network problem. Experimental results show that the proposed algorithm outperforms others with higher performance.
Tools and Algorithms for Construction and Analysis of Systems
DEFF Research Database (Denmark)
This book constitutes the refereed proceedings of the 6th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2000, held as part of ETAPS 2000 in Berlin, Germany, in March/April 2000. The 33 revised full papers presented together with one invited...... paper and two short tool descriptions were carefully reviewed and selected from a total of 107 submissions. The papers are organized in topical sections on software and formal methods, formal methods, timed and hybrid systems, infinite and parameterized systems, diagnostic and test generation, efficient...
New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems
Directory of Open Access Journals (Sweden)
Xiguang Li
2017-01-01
Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.
Development of a general learning algorithm with applications in nuclear reactor systems
Energy Technology Data Exchange (ETDEWEB)
Brittain, C.R.; Otaduy, P.J.; Perez, R.B.
1989-12-01
The objective of this study was development of a generalized learning algorithm that can learn to predict a particular feature of a process by observation of a set of representative input examples. The algorithm uses pattern matching and statistical analysis techniques to find a functional relationship between descriptive attributes of the input examples and the feature to be predicted. The algorithm was tested by applying it to a set of examples consisting of performance descriptions for 277 fuel cycles of Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). The program learned to predict the critical rod position for the HFIR from core configuration data prior to reactor startup. The functional relationship bases its predictions on initial core reactivity, the number of certain targets placed in the center of the reactor, and the total exposure of the control plates. Twelve characteristic fuel cycle clusters were identified. Nine fuel cycles were diagnosed as having noisy data, and one could not be predicted by the functional relationship. 13 refs., 6 figs.
Development of a general learning algorithm with applications in nuclear reactor systems
International Nuclear Information System (INIS)
Brittain, C.R.; Otaduy, P.J.; Perez, R.B.
1989-12-01
The objective of this study was development of a generalized learning algorithm that can learn to predict a particular feature of a process by observation of a set of representative input examples. The algorithm uses pattern matching and statistical analysis techniques to find a functional relationship between descriptive attributes of the input examples and the feature to be predicted. The algorithm was tested by applying it to a set of examples consisting of performance descriptions for 277 fuel cycles of Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). The program learned to predict the critical rod position for the HFIR from core configuration data prior to reactor startup. The functional relationship bases its predictions on initial core reactivity, the number of certain targets placed in the center of the reactor, and the total exposure of the control plates. Twelve characteristic fuel cycle clusters were identified. Nine fuel cycles were diagnosed as having noisy data, and one could not be predicted by the functional relationship. 13 refs., 6 figs
An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning
Directory of Open Access Journals (Sweden)
Xiaohui Yan
2012-01-01
Full Text Available Bacterial Foraging Algorithm (BFO is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2013-03-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
Parallel DC3 Algorithm for Suffix Array Construction on Many-Core Accelerators
Liao, Gang
2015-05-01
In bioinformatics applications, suffix arrays are widely used to DNA sequence alignments in the initial exact match phase of heuristic algorithms. With the exponential growth and availability of data, using many-core accelerators, like GPUs, to optimize existing algorithms is very common. We present a new implementation of suffix array on GPU. As a result, suffix array construction on GPU achieves around 10x speedup on standard large data sets, which contain more than 100 million characters. The idea is simple, fast and scalable that can be easily scale to multi-core processors and even heterogeneous architectures. © 2015 IEEE.
Parallel DC3 Algorithm for Suffix Array Construction on Many-Core Accelerators
Liao, Gang; Ma, Longfei; Zang, Guangming; Tang, Lin
2015-01-01
In bioinformatics applications, suffix arrays are widely used to DNA sequence alignments in the initial exact match phase of heuristic algorithms. With the exponential growth and availability of data, using many-core accelerators, like GPUs, to optimize existing algorithms is very common. We present a new implementation of suffix array on GPU. As a result, suffix array construction on GPU achieves around 10x speedup on standard large data sets, which contain more than 100 million characters. The idea is simple, fast and scalable that can be easily scale to multi-core processors and even heterogeneous architectures. © 2015 IEEE.
Huang, Tao; Li, Xiao-yu; Jin, Rui; Ku, Jing; Xu, Sen-miao; Xu, Meng-ling; Wu, Zhen-zhong; Kong, De-guo
2015-04-01
The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and
Al-Refaie, Ahmed F.; Tennyson, Jonathan
2017-12-01
Construction and diagonalization of the Hamiltonian matrix is the rate-limiting step in most low-energy electron - molecule collision calculations. Tennyson (1996) implemented a novel algorithm for Hamiltonian construction which took advantage of the structure of the wavefunction in such calculations. This algorithm is re-engineered to make use of modern computer architectures and the use of appropriate diagonalizers is considered. Test calculations demonstrate that significant speed-ups can be gained using multiple CPUs. This opens the way to calculations which consider higher collision energies, larger molecules and / or more target states. The methodology, which is implemented as part of the UK molecular R-matrix codes (UKRMol and UKRMol+) can also be used for studies of bound molecular Rydberg states, photoionization and positron-molecule collisions.
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. Copyright © 2016. Published by Elsevier B.V.
CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms
Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert
2018-04-01
CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.
How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Directory of Open Access Journals (Sweden)
Jenna Burrell
2016-01-01
Full Text Available This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1 opacity as intentional corporate or state secrecy, (2 opacity as technical illiteracy, and (3 an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm.
Active Learning of Markov Decision Processes for System Verification
DEFF Research Database (Denmark)
Chen, Yingke; Nielsen, Thomas Dyhre
2012-01-01
deterministic Markov decision processes from data by actively guiding the selection of input actions. The algorithm is empirically analyzed by learning system models of slot machines, and it is demonstrated that the proposed active learning procedure can significantly reduce the amount of data required...... demanding process, and this shortcoming has motivated the development of algorithms for automatically learning system models from observed system behaviors. Recently, algorithms have been proposed for learning Markov decision process representations of reactive systems based on alternating sequences...... of input/output observations. While alleviating the problem of manually constructing a system model, the collection/generation of observed system behaviors can also prove demanding. Consequently we seek to minimize the amount of data required. In this paper we propose an algorithm for learning...
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.
Genetic algorithm optimization for dynamic construction site layout planning
Directory of Open Access Journals (Sweden)
Farmakis Panagiotis M.
2018-02-01
Full Text Available The dynamic construction site layout planning (DCSLP problem refers to the efficient placement and relocation of temporary construction facilities within a dynamically changing construction site environment considering the characteristics of facilities and work interrelationships, the shape and topography of the construction site, and the time-varying project needs. A multi-objective dynamic optimization model is developed for this problem that considers construction and relocation costs of facilities, transportation costs of resources moving from one facility to another or to workplaces, as well as safety and environmental considerations resulting from facilities’ operations and interconnections. The latter considerations are taken into account in the form of preferences or constraints regarding the proximity or remoteness of particular facilities to other facilities or work areas. The analysis of multiple project phases and the dynamic facility relocation from phase to phase highly increases the problem size, which, even in its static form, falls within the NP (for Nondeterministic Polynomial time- hard class of combinatorial optimization problems. For this reason, a genetic algorithm has been implemented for the solution due to its capability to robustly search within a large solution space. Several case studies and operational scenarios have been implemented through the Palisade’s Evolver software for model testing and evaluation. The results indicate satisfactory model response to time-varying input data in terms of solution quality and computation time. The model can provide decision support to site managers, allowing them to examine alternative scenarios and fine-tune optimal solutions according to their experience by introducing desirable preferences or constraints in the decision process.
A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.
Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei
2017-05-18
The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.
Bejnordi, Babak Ehteshami; Veta, Mitko; van Diest, Paul Johannes; Van Ginneken, Bram; Karssemeijer, Nico; Litjens, Geert; van der Laak, Jeroen A.W.M.; Hermsen, Meyke; Manson, Quirine F.; Balkenhol, Maschenka; Geessink, Oscar; Stathonikos, Nikolaos; Van Dijk, Marcory C.R.F.; Bult, Peter; Beca, Francisco; Beck, Andrew H.; Wang, Dayong; Khosla, Aditya; Gargeya, Rishab; Irshad, Humayun; Zhong, Aoxiao; Dou, Qi; Li, Quanzheng; Chen, Hao; Lin, Huang Jing; Heng, Pheng Ann; Haß, Christian; Bruni, Elia; Wong, Quincy; Halici, Ugur; Öner, Mustafa Ümit; Cetin-Atalay, Rengul; Berseth, Matt; Khvatkov, Vitali; Vylegzhanin, Alexei; Kraus, Oren; Shaban, Muhammad; Rajpoot, Nasir; Awan, Ruqayya; Sirinukunwattana, Korsuk; Qaiser, Talha; Tsang, Yee Wah; Tellez, David; Annuscheit, Jonas; Hufnagl, Peter; Valkonen, Mira; Kartasalo, Kimmo; Latonen, Leena; Ruusuvuori, Pekka; Liimatainen, Kaisa
2017-01-01
IMPORTANCE: Application of deep learning algorithms to whole-slide pathology imagescan potentially improve diagnostic accuracy and efficiency. OBJECTIVE: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph
Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning
Duan, Haibin; Li, Pei; Shi, Yuhui; Zhang, Xiangyin; Sun, Changhao
2015-01-01
This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the…
An augmented Lagrangian multi-scale dictionary learning algorithm
Directory of Open Access Journals (Sweden)
Ye Meng
2011-01-01
Full Text Available Abstract Learning overcomplete dictionaries for sparse signal representation has become a hot topic fascinated by many researchers in the recent years, while most of the existing approaches have a serious problem that they always lead to local minima. In this article, we present a novel augmented Lagrangian multi-scale dictionary learning algorithm (ALM-DL, which is achieved by first recasting the constrained dictionary learning problem into an AL scheme, and then updating the dictionary after each inner iteration of the scheme during which majorization-minimization technique is employed for solving the inner subproblem. Refining the dictionary from low scale to high makes the proposed method less dependent on the initial dictionary hence avoiding local optima. Numerical tests for synthetic data and denoising applications on real images demonstrate the superior performance of the proposed approach.
An E-Learning Environment for Algorithmic: Toward an Active Construction of Skills
Babori, Abdelghani; Fassi, Hicham Fihri; Hariri, Abdellah; Bideq, Mustapha
2016-01-01
Assimilating an algorithmic course is a persistent problem for many undergraduate students. The major problem faced by students is the lack of problem solving ability and flexibility. Therefore, students are generally passive, unmotivated and unable to mobilize all the acquired knowledge (loops, test, variables, etc.) to deal with new encountered…
A numeric comparison of variable selection algorithms for supervised learning
International Nuclear Information System (INIS)
Palombo, G.; Narsky, I.
2009-01-01
Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.
Seghouane, Abd-Krim; Iqbal, Asif
2017-09-01
Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.
Inference algorithms and learning theory for Bayesian sparse factor analysis
International Nuclear Information System (INIS)
Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John
2009-01-01
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.
Inference algorithms and learning theory for Bayesian sparse factor analysis
Energy Technology Data Exchange (ETDEWEB)
Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)
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.
Current Perspectives on the Pedagogical Value of Algorithm Visualization
DEFF Research Database (Denmark)
Demetriadis, Stavros N.; Papadopoulos, Pantelis M.
2004-01-01
and presenting significant research results concerning their pedagogical efficiency. Available studies indicate that it is not the quality of the graphical display (“what the students see”) but students’ engagement in active learning situations with algorithm visualization systems (“what the students do...... of the software it seems that “low-tech and fidelity” AV construction systems may be quite adequate for supporting students’ engagement in essential learning activities....
International Nuclear Information System (INIS)
Roche-Lima, Abiel; Thulasiram, Ruppa K
2012-01-01
Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.
Gaur, Pallavi; Chaturvedi, Anoop
2017-07-22
The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.
Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs
Directory of Open Access Journals (Sweden)
Vaughn Matthew
2010-11-01
-directed de Bruijn graph is a fundamental data structure for any sequence assembly program based on Eulerian approach. Our algorithms for constructing Bi-directed de Bruijn graphs are efficient in parallel and out of core settings. These algorithms can be used in building large scale bi-directed de Bruijn graphs. Furthermore, our algorithms do not employ any all-to-all communications in a parallel setting and perform better than the prior algorithms. Finally our out-of-core algorithm is extremely memory efficient and can replace the existing graph construction algorithm in VELVET.
Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs.
Kundeti, Vamsi K; Rajasekaran, Sanguthevar; Dinh, Hieu; Vaughn, Matthew; Thapar, Vishal
2010-11-15
any sequence assembly program based on Eulerian approach. Our algorithms for constructing Bi-directed de Bruijn graphs are efficient in parallel and out of core settings. These algorithms can be used in building large scale bi-directed de Bruijn graphs. Furthermore, our algorithms do not employ any all-to-all communications in a parallel setting and perform better than the prior algorithms. Finally our out-of-core algorithm is extremely memory efficient and can replace the existing graph construction algorithm in VELVET.
Designing anticancer peptides by constructive machine learning.
Grisoni, Francesca; Neuhaus, Claudia; Gabernet, Gisela; Müller, Alex; Hiss, Jan; Schneider, Gisbert
2018-04-21
Constructive machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a generative model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on alpha-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Tools and Algorithms for the Construction and Analysis of Systems
DEFF Research Database (Denmark)
This book constitutes the refereed proceedings of the 10th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2004, held in Barcelona, Spain in March/April 2004. The 37 revised full papers and 6 revised tool demonstration papers presented were...... carefully reviewed and selected from a total of 162 submissions. The papers are organized in topical sections on theorem proving, probabilistic model checking, testing, tools, explicit state and Petri nets, scheduling, constraint solving, timed systems, case studies, software, temporal logic, abstraction...
Construction Method of Display Proposal for Commodities in Sales Promotion by Genetic Algorithm
Yumoto, Masaki
In a sales promotion task, wholesaler prepares and presents the display proposal for commodities in order to negotiate with retailer's buyers what commodities they should sell. For automating the sales promotion tasks, the proposal has to be constructed according to the target retailer's buyer. However, it is difficult to construct the proposal suitable for the target retail store because of too much combination of commodities. This paper proposes a construction method by Genetic algorithm (GA). The proposed method represents initial display proposals for commodities with genes, improve ones with the evaluation value by GA, and rearrange one with the highest evaluation value according to the classification of commodity. Through practical experiment, we can confirm that display proposal by the proposed method is similar with the one constructed by a wholesaler.
International Nuclear Information System (INIS)
Paszkowicz, Wojciech
2006-01-01
Genetic algorithms represent a powerful global-optimisation tool applicable in solving tasks of high complexity in science, technology, medicine, communication, etc. The usual genetic-algorithm calculation scheme is extended here by introduction of a quadratic self-learning operator, which performs a partial local search for randomly selected representatives of the population. This operator is aimed as a minor deterministic contribution to the (stochastic) genetic search. The population representing the trial solutions is split into two equal subpopulations allowed to exhibit different mutation rates (so called asymmetric mutation). The convergence is studied in detail exploiting a crystallographic-test example of indexing of powder diffraction data of orthorhombic lithium copper oxide, varying such parameters as mutation rates and the learning rate. It is shown through the averaged (over the subpopulation) fitness behaviour, how the genetic diversity in the population depends on the mutation rate of the given subpopulation. Conditions and algorithm parameter values favourable for convergence in the framework of proposed approach are discussed using the results for the mentioned example. Further data are studied with a somewhat modified algorithm using periodically varying mutation rates and a problem-specific operator. The chance of finding the global optimum and the convergence speed are observed to be strongly influenced by the effective mutation level and on the self-learning level. The optimal values of these two parameters are about 6 and 5%, respectively. The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm. The results of the study confirm that the applied methodology leads to improvement of the classical genetic algorithm and, therefore, it is expected to be helpful in constructing of algorithms permitting to solve similar tasks of higher complexity
Narula, Sukrit; Shameer, Khader; Salem Omar, Alaa Mabrouk; Dudley, Joel T; Sengupta, Partho P
2016-11-29
Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation. Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p 13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain. Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
C. Sunil Kumar
2014-07-01
Full Text Available Automation of descriptive answers evaluation is the need of the hour because of the huge increase in the number of students enrolling each year in educational institutions and the limited staff available to spare their time for evaluations. In this paper, we use a machine learning workbench called LightSIDE to accomplish auto evaluation and scoring of descriptive answers. We attempted to identify the best supervised machine learning algorithm given a limited training set sample size scenario. We evaluated performances of Bayes, SVM, Logistic Regression, Random forests, Decision stump and Decision trees algorithms. We confirmed SVM as best performing algorithm based on quantitative measurements across accuracy, kappa, training speed and prediction accuracy with supplied test set.
Immune Genetic Learning of Fuzzy Cognitive Map
Institute of Scientific and Technical Information of China (English)
LIN Chun-mei; HE Yue; TANG Bing-yong
2006-01-01
This paper presents a hybrid methodology of automatically constructing fuzzy cognitive map (FCM). The method uses immune genetic algorithm to learn the connection matrix of FCM. In the algorithm, the DNA coding method is used and an immune operator based on immune mechanism is constructed. The characteristics of the system and the experts' knowledge are abstracted as vaccine for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.
An Educational Data Mining Approach to Concept Map Construction for Web based Learning
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Anal ACHARYA
2017-01-01
Full Text Available This aim of this article is to study the use of Educational Data Mining (EDM techniques in constructing concept maps for organizing knowledge in web based learning systems whereby studying their synergistic effects in enhancing learning. This article first provides a tutorial based introduction to EDM. The applicability of web based learning systems in enhancing the efficiency of EDM techniques in real time environment is investigated. Web based learning systems often use a tool for organizing knowledge. This article explores the use of one such tool called concept map for this purpose. The pioneering works by various researchers who proposed web based learning systems in personalized and collaborative environment in this arena are next presented. A set of parameters are proposed based on which personalized and collaborative learning applications may be generalized and their performances compared. It is found that personalized learning environment uses EDM techniques more exhaustively compared to collaborative learning for concept map construction in web based environment. This article can be used as a starting point for freshers who would like to use EDM techniques for concept map construction for web based learning purposes.
Mean-field theory of meta-learning
International Nuclear Information System (INIS)
Plewczynski, Dariusz
2009-01-01
We discuss here the mean-field theory for a cellular automata model of meta-learning. Meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents that acquire and process incoming information using various types, or different versions, of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share the opposite classification outcome can be observed in the system. Therefore, the probability of selecting a proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are built from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents
Artrith, Nongnuch; Urban, Alexander; Ceder, Gerbrand
2018-06-01
The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANNs). We show for the example of the amorphous LiSi alloy that around 1000 first-principles calculations are sufficient for the ANN-potential assisted sampling of low-energy atomic configurations in the entire amorphous LixSi phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of LixSi configurations using molecular dynamics simulations and a general ANN potential trained to ˜45 000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials.
Sampling algorithms for validation of supervised learning models for Ising-like systems
Portman, Nataliya; Tamblyn, Isaac
2017-12-01
In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).
Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm
International Nuclear Information System (INIS)
Rao, R.V.; More, K.C.
2015-01-01
Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence
International Nuclear Information System (INIS)
Machado, Marcelo Dornellas
1999-04-01
Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. In this work, a new learning mode, to be used by the Population-Based Incremental Learning (PBIL) algorithm, who combines mechanisms of standard genetic algorithm with simple competitive learning, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process know as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors. This problem can be interpreted as search of a load pattern to be used in the nucleus of the reactor in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)
Core story creation: analysing narratives to construct stories for learning.
Petty, Julia; Jarvis, Joy; Thomas, Rebecca
2018-03-16
Educational research uses narrative enquiry to gain and interpret people's experiences. Narrative analysis is used to organise and make sense of acquired narrative. 'Core story creation' is a way of managing raw data obtained from narrative interviews to construct stories for learning. To explain how core story creation can be used to construct stories from raw narratives obtained by interviewing parents about their neonatal experiences and then use these stories to educate learners. Core story creation involves reconfiguration of raw narratives. Reconfiguration includes listening to and rereading transcribed narratives, identifying elements of 'emplotment' and reordering these to form a constructed story. Thematic analysis is then performed on the story to draw out learning themes informed by the participants. Core story creation using emplotment is a strategy of narrative reconfiguration that produces stories which can be used to develop resources relating to person-centred education about the patient experience. Stories constructed from raw narratives in the context of constructivism can provide a medium or an 'end product' for use in learning resource development. This can then contribute to educating students or health professionals about patients' experiences. ©2018 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.
Lago Garcia, J.; De Ridder, Fjo; De Schutter, B.H.K.
2018-01-01
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning
Optimization of internet content filtering-Combined with KNN and OCAT algorithms
Guo, Tianze; Wu, Lingjing; Liu, Jiaming
2018-04-01
The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.
Directory of Open Access Journals (Sweden)
Jiří Fejfar
2012-01-01
Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.
Constructing Aligned Assessments Using Automated Test Construction
Porter, Andrew; Polikoff, Morgan S.; Barghaus, Katherine M.; Yang, Rui
2013-01-01
We describe an innovative automated test construction algorithm for building aligned achievement tests. By incorporating the algorithm into the test construction process, along with other test construction procedures for building reliable and unbiased assessments, the result is much more valid tests than result from current test construction…
Head pose estimation algorithm based on deep learning
Cao, Yuanming; Liu, Yijun
2017-05-01
Head pose estimation has been widely used in the field of artificial intelligence, pattern recognition and intelligent human-computer interaction and so on. Good head pose estimation algorithm should deal with light, noise, identity, shelter and other factors robustly, but so far how to improve the accuracy and robustness of attitude estimation remains a major challenge in the field of computer vision. A method based on deep learning for pose estimation is presented. Deep learning with a strong learning ability, it can extract high-level image features of the input image by through a series of non-linear operation, then classifying the input image using the extracted feature. Such characteristics have greater differences in pose, while they are robust of light, identity, occlusion and other factors. The proposed head pose estimation is evaluated on the CAS-PEAL data set. Experimental results show that this method is effective to improve the accuracy of pose estimation.
Roelle, Julian; Müller, Claudia; Roelle, Detlev; Berthold, Kirsten
2015-01-01
Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which interactive learning activities are at the top; these are then followed by constructive and active learning activities, respectively. Against this background, we combined instructional explanations with different types of prompts that were designed to elicit these learning activities and tested the central predictions of the active-constructive-interactive framework. In Experiment 1, N = 83 students were randomly assigned to one of four combinations of instructional explanations and prompts. To test the active learning hypothesis, the learners received either (1) complete explanations and engaging prompts designed to elicit active activities or (2) explanations that were reduced by inferences and inference prompts designed to engage learners in constructing the withheld information. Furthermore, in order to explore how interactive learning activities can be elicited, we gave the learners who had difficulties in constructing the prompted inferences adapted remedial explanations with either (3) unspecific engaging prompts or (4) revision prompts. In support of the active learning hypothesis, we found that the learners who received reduced explanations and inference prompts outperformed the learners who received complete explanations and engaging prompts. Moreover, revision prompts were more effective in eliciting interactive learning activities than engaging prompts. In Experiment 2, N = 40 students were randomly assigned to either (1) a reduced explanations and inference prompts or (2) a reduced explanations and inference prompts plus adapted remedial explanations and revision prompts condition. In support of the constructive learning hypothesis, the learners who received
E-Learning System for Design and Construction of Amplifier Using Transistors
Takemura, Atsushi
2014-01-01
This paper proposes a novel e-Learning system for the comprehensive understanding of electronic circuits with transistors. The proposed e-Learning system allows users to learn a wide range of topics, encompassing circuit theories, design, construction, and measurement. Given the fact that the amplifiers with transistors are an integral part of…
Separation of pulsar signals from noise using supervised machine learning algorithms
Bethapudi, S.; Desai, S.
2018-04-01
We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pipeline. This dataset was previously used for the cross-validation of the SPINN-based machine learning engine, obtained from the reprocessing of the HTRU-S survey data (Morello et al., 2014). We have used the Synthetic Minority Over-sampling Technique (SMOTE) to deal with high-class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean for both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in Morello et al. (2014), for the same recall value.
Methods for control over learning individual trajectory
Mitsel, A. A.; Cherniaeva, N. V.
2015-09-01
The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.
Directory of Open Access Journals (Sweden)
Marc Wieland
2014-03-01
Full Text Available In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS and very high resolution (WorldView-2, Quickbird, Ikonos multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based, have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.
International Nuclear Information System (INIS)
Lima, Alan M.M. de; Schirru, Roberto
2000-01-01
Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. The purpose of this work is to introduce a new parallelization method to be applied to the Population-Based Incremental Learning (PBIL) algorithm. PBIL combines standard genetic algorithm mechanisms with simple competitive learning and has ben successfully used in combinatorial optimization problems. The development of this algorithm aims its application to the reload optimization of PWR nuclear reactors. Tests have been performed with combinatorial optimization problems similar to the reload problem. Results are compared to the serial PBIL ones, showing the new method's superiority and its viability as a tool for the nuclear core reload problem solution. (author)
Bandyopadhyay, Sanghamitra
2007-01-01
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.
A Computer Environment for Beginners' Learning of Sorting Algorithms: Design and Pilot Evaluation
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…
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
Energy Technology Data Exchange (ETDEWEB)
Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)
2014-08-15
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka
2014-01-01
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology
Exploring Representativeness and Informativeness for Active Learning.
Du, Bo; Wang, Zengmao; Zhang, Lefei; Zhang, Liangpei; Liu, Wei; Shen, Jialie; Tao, Dacheng
2017-01-01
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified best-versus-second-best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.
An efficient dictionary learning algorithm and its application to 3-D medical image denoising.
Li, Shutao; Fang, Leyuan; Yin, Haitao
2012-02-01
In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE
Directory of Open Access Journals (Sweden)
Mojtaba Salehi
2013-03-01
Full Text Available In recent years, the explosion of learning materials in the web-based educational systems has caused difficulty of locating appropriate learning materials to learners. A personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable materials to learners. Since users express their opinions based on some specific attributes of items, this paper proposes a hybrid recommender system for learning materials based on their attributes to improve the accuracy and quality of recommendation. The presented system has two main modules: explicit attribute-based recommender and implicit attribute-based recommender. In the first module, weights of implicit or latent attributes of materials for learner are considered as chromosomes in genetic algorithm then this algorithm optimizes the weights according to historical rating. Then, recommendation is generated by Nearest Neighborhood Algorithm (NNA using the optimized weight vectors implicit attributes that represent the opinions of learners. In the second, preference matrix (PM is introduced that can model the interests of learner based on explicit attributes of learning materials in a multidimensional information model. Then, a new similarity measure between PMs is introduced and recommendations are generated by NNA. The experimental results show that our proposed method outperforms current algorithms on accuracy measures and can alleviate some problems such as cold-start and sparsity.
Machine Learning for Slow but Steady Interplanetary Construction
Agogino, Adrian
2017-01-01
For prolonged manned missions to destinations such as the moon and Mars, there is a need for significant infrastructure construction ahead of time, such as habitats and landing pads. Unfortunately we have little experience in remote construction and using conventional methods is likely to be expensive, cumbersome and unreliable. Fortunately these challenges may be overcome by taking advantage of the long lead time for such missions and using teams of small and slow construction robots. We propose using teams of simple autonomous robots for this purpose that would perform continuous construction over a period of many years or even decades. While individual robot reliability will be low over such long time frames, system reliability will be maintained by using machine learning over simulations to achieve coordination and reconfigurations in the event of lost robots.
Stimulating Learning with Integrated Assessments in Construction Education
Directory of Open Access Journals (Sweden)
Imriyas Kamardeen
2014-09-01
Full Text Available Quality of learning students experience is heavily dependent on the effectiveness of course design. Assessments are a key component in course design and students determine their level of involvement in a learning activity based on whether it is assessed or not. Assessments are therefore a powerful tool that lecturers can utilise to drive learning. However, designing effective assessments to stimulate learning is challenging in the presence of disciplinary, contextual dimensions. A case study approach is adopted to demonstrate how effective integrated assessment schemes may be developed and implemented for construction education. The scheme in the case study amalgamated case-based learning, online quizzes and adaptive eTests to provide a variety of assessments, aligned with lecture topics and contemporary real-word scenarios. It was found that the presence of both formative and summative tasks in the assessment scheme complemented each other, kept students constantly motivated and engaged in learning, and resulted in a good learning experience for them. The study provide evidence, and valuable insights and tips for lecturers in similar degree programs as to how they could modify pedagogical styles in their courses for better learning experiences for students and improved teaching ratings for themselves. Keywords: Pedagogy, Integrated assessment, Case-based learning, Online quiz, Adaptive eLearning
Stimulating Learning with Integrated Assessments in Construction Education
Directory of Open Access Journals (Sweden)
Imriyas Kamardeen
2014-09-01
Full Text Available Quality of learning students experience is heavily dependent on the effectiveness of course design. Assessments are a key component in course design and students determine their level of involvement in a learning activity based on whether it is assessed or not. Assessments are therefore a powerful tool that lecturers can utilise to drive learning. However, designing effective assessments to stimulate learning is challenging in the presence of disciplinary, contextual dimensions. A case study approach is adopted to demonstrate how effective integrated assessment schemes may be developed and implemented for construction education. The scheme in the case study amalgamated case-based learning, online quizzes and adaptive eTests to provide a variety of assessments, aligned with lecture topics and contemporary real-word scenarios. It was found that the presence of both formative and summative tasks in the assessment scheme complemented each other, kept students constantly motivated and engaged in learning, and resulted in a good learning experience for them. The study provide evidence, and valuable insights and tips for lecturers in similar degree programs as to how they could modify pedagogical styles in their courses for better learning experiences for students and improved teaching ratings for themselves. Keywords: Pedagogy, Integrated assessment, Case-based learning, Online quiz, Adaptive eLearning
Mixed Methods Study Using Constructive Learning Team Model for Secondary Mathematics Teachers
Ritter, Kristy L.
2010-01-01
The constructive learning team model for secondary mathematics teachers (CLTM) was created to provide students with learning opportunities and experiences that address deficiencies in oral and written communication, logical processes and analysis, mathematical operations, independent learning, teamwork, and technology utilization. This study…
Examining Collaborative Knowledge Construction in Microblogging-Based Learning Environments
Directory of Open Access Journals (Sweden)
Tian Luo
2017-09-01
Full Text Available Aim/Purpose: The purpose of the study is to provide foundational research to exemplify how knowledge construction takes place in microblogging-based learning environments, to understand learner interaction representing the knowledge construction process, and to analyze learner perception, thereby suggesting a model of delivery for microblogging. Background: Up-and-coming digital native learners crave the real-time, multimedia, global-interconnectedness of microblogging, yet there has been limited research that specifically proposes a working model of Twitter’s classroom integration for designers and practitioners without bundling it in with other social media tools. Methodology: This semester-long study utilized a case-study research design via a multi-dimensional approach in a hybrid classroom with both face-to-face and online environments. Tweets were collected from four types of activities and coded based on content within their contextual setting. Twenty-four college students participated in the study. Contribution: The findings shed light on the process of knowledge construction in mi-croblogging and reveal key types of knowledge manifested during learning activities. The study also proposes a model for delivering microblogging to formal learning environments applicable to various contexts for designers and practitioners. Findings: There are distinct learner interaction patterns representing the process of knowledge construction in microblogging activities ranging from low-order to high-order cognitive tasks. Students generally were in favor of the Twitter integration in this study. Recommendations for Practitioners: The three central activities (exploring hashtags, discussion topics, and participating in live chats along with the backchannel activity formulate a working model that represents the sequential process of Twitter integration into classrooms. Impact on Society: Microblogging allows learners omnichannel access while hashtags
Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
Paganini, Michela; The ATLAS collaboration
2017-01-01
The separation of $b$-quark initiated jets from those coming from lighter quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.
MODIS Science Algorithms and Data Systems Lessons Learned
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.
A Study of Work Based Learning For Construction Building Workers
Siregar, Syafiatun
2018-03-01
Work-based learning (WBL) is designed to improve the competence of participants. This study aims to apply the WBL and to develop attitudes, knowledge, skills, behaviors, and habits, which in turn can improve the competence of construction workers in the field to be sampled. This research was conducted on building construction workers in Medan City with 30 research subjects. The results showed that the evaluation of learning increased in phase I obtained the difference of the average score of 20.9 (the meeting I) and 25.50 (meeting II). The final result shows that the level of activity and competence increased significantly after WBL
Végh, Ladislav
2016-01-01
The first data structure that first-year undergraduate students learn during the programming and algorithms courses is the one-dimensional array. For novice programmers, it might be hard to understand different algorithms on arrays (e.g. searching, mirroring, sorting algorithms), because the algorithms dynamically change the values of elements. In…
Construction of a Learning Agent Handling Its Rewards According to Environmental Situations
Moriyama, Koichi; Numao, Masayuki
The authors aim at constructing an agent which learns appropriate actions in a Multi-Agent environment with and without social dilemmas. For this aim, the agent must have nonrationality that makes it give up its own profit when it should do that. Since there are many studies on rational learning that brings more and more profit, it is desirable to utilize them for constructing the agent. Therefore, we use a reward-handling manner that makes internal evaluation from the agent's rewards, and then the agent learns actions by a rational learning method with the internal evaluation. If the agent has only a fixed manner, however, it does not act well in the environment with and without dilemmas. Thus, the authors equip the agent with several reward-handling manners and criteria for selecting an effective one for the environmental situation. In the case of humans, what generates the internal evaluation is usually called emotion. Hence, this study also aims at throwing light on emotional activities of humans from a constructive view. In this paper, we divide a Multi-Agent environment into three situations and construct an agent having the reward-handling manners and the criteria. We observe that the agent acts well in all the three Multi-Agent situations composed of homogeneous agents.
Bisele, Maria; Bencsik, Martin; Lewis, Martin G C; Barnett, Cleveland T
2017-01-01
Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg-1) and joint powers (W.kg-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors' knowledge, this is the first study to optimise the development of a machine learning algorithm.
Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal
2015-08-13
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.
Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang
2016-09-22
To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.
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. Copyright © 2015 Elsevier Ltd. All rights reserved.
Genetic Learning Particle Swarm Optimization.
Gong, Yue-Jiao; Li, Jing-Jing; Zhou, Yicong; Li, Yun; Chung, Henry Shu-Hung; Shi, Yu-Hui; Zhang, Jun
2016-10-01
Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.
A developmental approach to learning causal models for cyber security
Mugan, Jonathan
2013-05-01
To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.
Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
Paganini, Michela; The ATLAS collaboration
2017-01-01
The separation of b-quark initiated jets from those coming from lighter quark flavours (b-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful b-tagging algorithms combine information from low-level taggers exploiting reconstructed track and vertex information using a multivariate classifier. The potential of modern Machine Learning techniques such as Recurrent Neural Networks and Deep Learning is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.
Empirical Refinements of a Molecular Genetics Learning Progression: The Molecular Constructs
Todd, Amber; Kenyon, Lisa
2016-01-01
This article describes revisions to four of the eight constructs of the Duncan molecular genetics learning progression [Duncan, Rogat, & Yarden, (2009)]. As learning progressions remain hypothetical models until validated by multiple rounds of empirical studies, these revisions are an important step toward validating the progression. Our…
Project-Based Learning and Student Knowledge Construction during Asynchronous Online Discussion
Koh, Joyce Hwee Ling; Herring, Susan C.; Hew, Khe Foon
2010-01-01
Project-based learning engages students in problem solving through artefact design. However, previous studies of online project-based learning have focused primarily on the dynamics of online collaboration; students' knowledge construction throughout this process has not been examined thoroughly. This case study analyzed the relationship between…
An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Donghao Wang
2016-09-01
Full Text Available To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.
A system for learning statistical motion patterns.
Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve
2006-09-01
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.
Promoting Constructive Activities that Support Vicarious Learning during Computer-Based Instruction
Gholson, Barry; Craig, Scotty D.
2006-01-01
This article explores several ways computer-based instruction can be designed to support constructive activities and promote deep-level comprehension during vicarious learning. Vicarious learning, discussed in the first section, refers to knowledge acquisition under conditions in which the learner is not the addressee and does not physically…
Cross entropy-based memetic algorithms: An application study over the tool switching problem
Directory of Open Access Journals (Sweden)
Jhon Edgar Amaya
2013-05-01
Full Text Available This paper presents a parameterized schema for building memetic algorithms based on cross-entropy (CE methods. This novel schema is general in nature, and features multiple probability mass functions and Lamarckian learning. The applicability of the approach is assessed by considering the Tool Switching Problem, a complex combinatorial problem in the field of Flexible Manufacturing Systems. An exhaustive evaluation (including techniques ranging from local search and evolutionary algorithms to constructive methods provides evidence of the effectiveness of CE-based memetic algorithms.
Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder
Directory of Open Access Journals (Sweden)
Detian Huang
2018-01-01
Full Text Available Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application’s requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high- and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.
Use of Constructed-Response Questions to Support Learning of Cell Biology during Lectures
Directory of Open Access Journals (Sweden)
Foong May Yeong
2015-02-01
Full Text Available The use of class-response systems such as the Clickers to promote active-learning during lectures has been wide-spread. However, the often-used MCQ format in class activities as well as in assessments for large classes might lower students’ expectations and attitudes towards learning. Here, I describe my experience converting MCQs to constructed-response questions for in-class learning activities by removing cues from the MCQs. From the responses submitted, students seemed capable of providing answers without the need for cues. Using class-response systems such as Socrative for such constructed-response questions could be useful to challenge students to express their ideas in their own words. Moreover, by constructing their own answers, mis-conceptions could be revealed and corrected in a timely manner.
The Learner’s learning evironment: three drivers that impact on its construction and use
Ellis, Ainslie Elizabeth
2017-01-01
With universities adopting a learner-centered, constructivist approach to learning and greater use of technology, combined with the pressures of 21st century living and the increasing number of people taking up tertiary study, there is a greater need than ever to determine how a learner constructs and uses his/her learning environment, and what drives that process of construction and use. This thesis investigates the learner’s learning environment from the learner’s perspective. Through the a...
Cooperative learning benefits scale: construction and validation studies
Directory of Open Access Journals (Sweden)
José Lopes
2014-07-01
Full Text Available The aim of this study was to develop and validate a scale of benefits of the Cooperative Learning (SBCL given the exiguity of instruments that evaluate these outputs of the method. The study resorted to a convenience sample comprised of 162 students, males and females, aged between 11 and 18 years. The final instrument has 23 items in a two-dimensional factor structure: psychological and academic benefits and social benefits. The results indicate that the SBCL present good psychometric properties (construct and discriminant validity and reliability. The results are discussed in light of the model of cooperative learning.
Deng, William Nanqiao; Wang, Shuo; Ventrici de Souza, Joao Francisco; Kuhl, Tonya L; Liu, Gang-Yu
2018-06-11
Scanning probe microscopy (SPM) such as atomic force microscopy (AFM) is widely known for high-resolution imaging of surface structures and nanolithography in two dimension (2D), which provides important physical insights in surface science and material science. This work reports a new algorithm to enable construction and display of layer-by-layer 3D structures from SPM images. The algorithm enables alignment of SPM images acquired during layer-by-layer deposition, removal of redundant features, and faithfully constructs the deposited 3D structures. The display uses a "see-through" strategy to enable the structure of each layer to be visible. The results demonstrate high spatial accuracy as well as algorithm versatility; users can set parameters for reconstruction and display as per image quality and research needs. To the best of our knowledge, this method represents the first report to enable SPM technology for 3D imaging construction and display. The detailed algorithm is provided to facilitate usage of the same approach in any SPM software. These new capabilities support wide applications of SPM that require 3D image reconstruction and display, such as 3D nanoprinting, and 3D additive and subtractive manufacturing and imaging.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2015-12-01
Full Text Available This paper presents the performance of teaching–learning-based optimization (TLBO algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH. The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching–learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.
Directory of Open Access Journals (Sweden)
Daniil S. Chivilikhin
2014-11-01
Full Text Available The procedure of testing traditionally used in software engineering cannot guarantee program correctness; therefore verification is used at the excess requirements to programs reliability. Verification makes it possible to check certain properties of programs in all possible computational states; however, this process is very complex. In the model checking method a model of the program is built (often, manually and requirements in terms of temporal logic are formulated. Such temporal properties of the model can be checked automatically. The main issue in this framework is the gap between the program and its model. Automata-based programming paradigm gives the possibility to overcome this limitation. In this paradigm, program logic is represented using finite-state machines. The advantage of finite-state machines is that their models can be constructed automatically. The paper deals with the application of mutation-based ant colony optimization algorithm to the problem of finite-state machine construction from their specification, defined by test scenarios and temporal properties. The presented approach has been tested on the elevator doors control problem as well as on randomly generated data. Obtained results show the ant colony algorithm is two-three times faster than the previously used genetic algorithm. The proposed approach can be recommended for inferring control programs for critical systems.
Directory of Open Access Journals (Sweden)
REIS, M V. S. de A.
2017-06-01
Full Text Available This paper aims to evaluate the use of machine learning techniques in a database of marine accidents. We analyzed and evaluated the main causes and types of marine accidents in the Northern Fluminense region. For this, machine learning techniques were used. The study showed that the modeling can be done in a satisfactory manner using different configurations of classification algorithms, varying the activation functions and training parameters. The SMO (Sequential Minimal Optimization algorithm showed the best performance result.
Cohort Learning Online in Graduate Higher Education: Constructing Knowledge in Cyber Community
Directory of Open Access Journals (Sweden)
Gabriele I. E. Strohschen
2004-01-01
Full Text Available This paper discusses a qualitative participatory action research study, which examined the nature of the cohort learning experience in an online masters program, from both faculty and student perspectives. After describing this online masters program in adult education designed from a social constructivist theoretical frame, this paper discusses two primary areas of findings related to cohort learning. First, were those related to the ongoing negotiation of the learning process: the importance of an opening residential; a consistent but flexible cohort structure; and building ongoing relationships. Second, were those related to the ongoing construction of knowledge: the role of team-teaching and the cohort model in transformative learning; the application of theory to real life practice, and the value of group support and collaboration in conducting research and constructing knowledge. Implications for practice are discussed.
Premaladha, J; Ravichandran, K S
2016-04-01
Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.
Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M.
2017-02-01
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ˜60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
International Nuclear Information System (INIS)
Nishizuka, N.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M.; Sugiura, K.
2017-01-01
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
Energy Technology Data Exchange (ETDEWEB)
Nishizuka, N.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M. [Applied Electromagnetic Research Institute, National Institute of Information and Communications Technology, 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795 (Japan); Sugiura, K., E-mail: nishizuka.naoto@nict.go.jp [Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology (Japan)
2017-02-01
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
Impedance learning for robotic contact tasks using natural actor-critic algorithm.
Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul
2010-04-01
Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.
Learning Earthquake Design and Construction 20. How do Beam ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 10; Issue 6. Learning Earthquake Design and Construction – How do Beam–Column Joints in RC Buildings Resist Earthquakes? C V R Murty. Classroom Volume 10 Issue 6 June 2005 pp 82-85 ...
Waragai, Ikumi; Ohta, Tatsuya; Kurabayashi, Shuichi; Kiyoki, Yasushi; Sato, Yukiko; Brückner, Stefan
2017-01-01
This paper presents the prototype of a foreign language learning space, based on the construction of an integrated formal/informal learning environment. Before the background of the continued innovation of information technology that places conventional learning styles and educational methods into new contexts based on new value-standards,…
A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.
Zhang, Aizhu; Sun, Genyun; Ren, Jinchang; Li, Xiaodong; Wang, Zhenjie; Jia, Xiuping
2018-01-01
Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.
Designing algorithm visualization on mobile platform: The proposed guidelines
Supli, A. A.; Shiratuddin, N.
2017-09-01
This paper entails an ongoing study about the design guidelines of algorithm visualization (AV) on mobile platform, helping students learning data structures and algorithm (DSA) subject effectively. Our previous review indicated that design guidelines of AV on mobile platform are still few. Mostly, previous guidelines of AV are developed for AV on desktop and website platform. In fact, mobile learning has been proved to enhance engagement in learning circumstances, and thus effect student's performance. In addition, the researchers highly recommend including UI design and Interactivity in designing effective AV system. However, the discussions of these two aspects in previous AV design guidelines are not comprehensive. The UI design in this paper describes the arrangement of AV features in mobile environment, whereas interactivity is about the active learning strategy features based on learning experiences (how to engage learners). Thus, this study main objective is to propose design guidelines of AV on mobile platform (AVOMP) that entails comprehensively UI design and interactivity aspects. These guidelines are developed through content analysis and comparative analysis from various related studies. These guidelines are useful for AV designers to help them constructing AVOMP for various topics on DSA.
Nagy, Ivan
2017-01-01
This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.
Predictive Variable Gain Iterative Learning Control for PMSM
Directory of Open Access Journals (Sweden)
Huimin Xu
2015-01-01
Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.
A comparison of algorithms for inference and learning in probabilistic graphical models.
Frey, Brendan J; Jojic, Nebojsa
2005-09-01
Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.
A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children
Sutoko, Stephanie; Funane, Tsukasa; Katura, Takusige; Sato, Hiroki; Kiguchi, Masashi; Maki, Atsushi; Monden, Yukifumi; Nagashima, Masako; Yamagata, Takanori; Dan, Ippeita
2017-02-01
In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm's performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection
AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM
International Nuclear Information System (INIS)
Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.
2015-01-01
In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.
Learning Earthquake Design and Construction – 23. Why are ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 10; Issue 11. Learning Earthquake Design and Construction – 23. Why are Buildings with Shear Walls Preferred in Seismic Regions? C V R Murty. Classroom Volume 10 Issue 11 November 2005 pp 85-88 ...
Cheng, Hong
2015-01-01
This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen
Boosting Learning Algorithm for Stock Price Forecasting
Wang, Chengzhang; Bai, Xiaoming
2018-03-01
To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.
Machine learning algorithms for mode-of-action classification in toxicity assessment.
Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can
2016-01-01
Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.
Directory of Open Access Journals (Sweden)
Deling Wang
2018-03-01
Full Text Available Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS, random forest (RF, and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.
Wang, Deling; Li, Jia-Rui; Zhang, Yu-Hang; Chen, Lei; Huang, Tao; Cai, Yu-Dong
2018-03-12
Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.
ON CONSTRUCTION OF A RELIABLE GROUND TRUTH FOR EVALUATION OF VISUAL SLAM ALGORITHMS
Directory of Open Access Journals (Sweden)
Jan Bayer
2016-11-01
Full Text Available In this work we are concerning the problem of localization accuracy evaluation of visual-based Simultaneous Localization and Mapping (SLAM techniques. Quantitative evaluation of the SLAM algorithm performance is usually done using the established metrics of Relative pose error and Absolute trajectory error which require a precise and reliable ground truth. Such a ground truth is usually hard to obtain, while it requires an expensive external localization system. In this work we are proposing to use the SLAM algorithm itself to construct a reliable ground-truth by offline frame-by-frame processing. The generated ground-truth is suitable for evaluation of different SLAM systems, as well as for tuning the parametrization of the on-line SLAM. The presented practical experimental results indicate the feasibility of the proposed approach.
Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint
Energy Technology Data Exchange (ETDEWEB)
Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Elgindy, Tarek [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dobbs, Alex [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-10-03
A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmospheric measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.
Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms
Directory of Open Access Journals (Sweden)
Kok-Lim Alvin Yau
2014-01-01
Full Text Available Cognitive radio (CR enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL, which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR.
Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning
Directory of Open Access Journals (Sweden)
Yingfeng Cai
2016-01-01
Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.
McIlvane, William J; Kledaras, Joanne B; Gerard, Christophe J; Wilde, Lorin; Smelson, David
2018-07-01
A few noteworthy exceptions notwithstanding, quantitative analyses of relational learning are most often simple descriptive measures of study outcomes. For example, studies of stimulus equivalence have made much progress using measures such as percentage consistent with equivalence relations, discrimination ratio, and response latency. Although procedures may have ad hoc variations, they remain fairly similar across studies. Comparison studies of training variables that lead to different outcomes are few. Yet to be developed are tools designed specifically for dynamic and/or parametric analyses of relational learning processes. This paper will focus on recent studies to develop (1) quality computer-based programmed instruction for supporting relational learning in children with autism spectrum disorders and intellectual disabilities and (2) formal algorithms that permit ongoing, dynamic assessment of learner performance and procedure changes to optimize instructional efficacy and efficiency. Because these algorithms have a strong basis in evidence and in theories of stimulus control, they may have utility also for basic and translational research. We present an overview of the research program, details of algorithm features, and summary results that illustrate their possible benefits. It also presents arguments that such algorithm development may encourage parametric research, help in integrating new research findings, and support in-depth quantitative analyses of stimulus control processes in relational learning. Such algorithms may also serve to model control of basic behavioral processes that is important to the design of effective programmed instruction for human learners with and without functional disabilities. Copyright © 2018 Elsevier B.V. All rights reserved.
Learning through delivery, Westinghouse AP1000 plant construction
International Nuclear Information System (INIS)
Gorgemans, J.; Hinman, R.D.; Steuck, C.M.; Greco, P.L.
2014-01-01
The AP1000 plant, which is a 1100 MWe class pressurized water reactor with passive safety features, is designed around a conventional 2 loop, 2 steam generator primary system configuration with 2 hot legs, 4 reactor coolant pumps directly mounted in the steam generator lower head and 4 cold legs. A particular feature of AP1000 is its modular construction to minimize the time and cost of construction. Modular construction allows activities to be run in parallel, it allows more activities to be performed in a controlled factory instead of in the field, and it provides a better level of quality. The AP1000 plant design includes 106 structural modules and 52 mechanical modules. Structural modules include all penetrations for piping, cable trays, HVAC duct runs, and all reinforcement for pipe, equipment hangers, and supports. Structural modules are shipped in sub-modules to support transportation by rail or truck or barge. Mechanical modules contain equipment such as pumps, tanks, heat exchangers, air-handling units, and filters along with interconnecting pipes, valves, instruments, wiring and support services. Modular construction requires strong coordination between engineering, supply chain and construction. A total of 8 AP1000 units are currently under construction in China and in the United States. The lessons learned and best practices of each new AP1000 construction are systematically incorporated into the standard design. (A.C.)
Beyond the "c" and the "x": Learning with Algorithms in Massive Open Online Courses (MOOCs)
Knox, Jeremy
2018-01-01
This article examines how algorithms are shaping student learning in massive open online courses (MOOCs). Following the dramatic rise of MOOC platform organisations in 2012, over 4,500 MOOCs have been offered to date, in increasingly diverse languages, and with a growing requirement for fees. However, discussions of "learning" in MOOCs…
Implementation of dictionary pair learning algorithm for image quality improvement
Vimala, C.; Aruna Priya, P.
2018-04-01
This paper proposes an image denoising on dictionary pair learning algorithm. Visual information is transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmissions is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image.
Robust head pose estimation via supervised manifold learning.
Wang, Chao; Song, Xubo
2014-05-01
Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations. Copyright © 2014 Elsevier Ltd. All rights reserved.
A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.
Directory of Open Access Journals (Sweden)
Fei Gao
Full Text Available For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a "soft-start" approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.
2017-01-01
Background Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P.05). Conclusions Machine learning algorithms can classify open-text feedback
Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John
2017-03-15
Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P.05). Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high
Greedy algorithm with weights for decision tree construction
Moshkov, Mikhail
2010-01-01
An approximate algorithm for minimization of weighted depth of decision trees is considered. A bound on accuracy of this algorithm is obtained which is unimprovable in general case. Under some natural assumptions on the class NP, the considered algorithm is close (from the point of view of accuracy) to best polynomial approximate algorithms for minimization of weighted depth of decision trees.
Greedy algorithm with weights for decision tree construction
Moshkov, Mikhail
2010-12-01
An approximate algorithm for minimization of weighted depth of decision trees is considered. A bound on accuracy of this algorithm is obtained which is unimprovable in general case. Under some natural assumptions on the class NP, the considered algorithm is close (from the point of view of accuracy) to best polynomial approximate algorithms for minimization of weighted depth of decision trees.
Development of fuzzy algorithm with learning function for nuclear steam generator level control
International Nuclear Information System (INIS)
Park, Gee Yong; Seong, Poong Hyun
1993-01-01
A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)
Jiang, Guo-Qing; Xu, Jing; Wei, Jun
2018-04-01
Two algorithms based on machine learning neural networks are proposed—the shallow learning (S-L) and deep learning (D-L) algorithms—that can potentially be used in atmosphere-only typhoon forecast models to provide flow-dependent typhoon-induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S-L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon-ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D-L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent-shaped SSTC distribution, with its large-scale pattern determined mainly by atmospheric factors (e.g., winds) and small-scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D-L algorithms improve maximum wind intensity errors by 60-70% for four case study simulations, compared to their atmosphere-only model runs.
Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
Directory of Open Access Journals (Sweden)
Muneki Yasuda
2018-04-01
Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.
Varzande, Mohsen
2015-01-01
Today, English education is very important but language learning has long been challenged since learning a second language is not only the mastery of its forms but also a process of identity construction and self-positioning in the second language. A review of recent studies shows that the cultural effects of learning English in the…
Comparison of two inductive learning methods: A case study in failed fuel identification
Energy Technology Data Exchange (ETDEWEB)
Reifman, J. [Argonne National Lab., IL (United States); Lee, J.C. [Michigan Univ., Ann Arbor, MI (United States). Dept. of Nuclear Engineering
1992-05-01
Two inductive learning methods, the ID3 and Rg algorithms, are studied as a means for systematically and automatically constructing the knowledge base of expert systems. Both inductive learning methods are general-purpose and use information entropy as a discriminatory measure in order to group objects of a common class. ID3 constructs a knowledge base by building decision trees that discriminate objects of a data set as a function of their class. Rg constructs a knowledge base by grouping objects of the same class into patterns or clusters. The two inductive methods are applied to the construction of a knowledge base for failed fuel identification in the Experimental Breeder Reactor II. Through analysis of the knowledge bases generated, the ID3 and Rg algorithms are compared for their knowledge representation, data overfitting, feature space partition, feature selection, and search procedure.
Comparison of two inductive learning methods: A case study in failed fuel identification
Energy Technology Data Exchange (ETDEWEB)
Reifman, J. (Argonne National Lab., IL (United States)); Lee, J.C. (Michigan Univ., Ann Arbor, MI (United States). Dept. of Nuclear Engineering)
1992-01-01
Two inductive learning methods, the ID3 and Rg algorithms, are studied as a means for systematically and automatically constructing the knowledge base of expert systems. Both inductive learning methods are general-purpose and use information entropy as a discriminatory measure in order to group objects of a common class. ID3 constructs a knowledge base by building decision trees that discriminate objects of a data set as a function of their class. Rg constructs a knowledge base by grouping objects of the same class into patterns or clusters. The two inductive methods are applied to the construction of a knowledge base for failed fuel identification in the Experimental Breeder Reactor II. Through analysis of the knowledge bases generated, the ID3 and Rg algorithms are compared for their knowledge representation, data overfitting, feature space partition, feature selection, and search procedure.
Comparison of two inductive learning methods: A case study in failed fuel identification
International Nuclear Information System (INIS)
Reifman, J.; Lee, J.C.
1992-01-01
Two inductive learning methods, the ID3 and Rg algorithms, are studied as a means for systematically and automatically constructing the knowledge base of expert systems. Both inductive learning methods are general-purpose and use information entropy as a discriminatory measure in order to group objects of a common class. ID3 constructs a knowledge base by building decision trees that discriminate objects of a data set as a function of their class. Rg constructs a knowledge base by grouping objects of the same class into patterns or clusters. The two inductive methods are applied to the construction of a knowledge base for failed fuel identification in the Experimental Breeder Reactor II. Through analysis of the knowledge bases generated, the ID3 and Rg algorithms are compared for their knowledge representation, data overfitting, feature space partition, feature selection, and search procedure
Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.
Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko
2018-05-04
Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population. Copyright © 2018 Elsevier Ltd. All rights reserved.
KAM Tori Construction Algorithms
Wiesel, W.
In this paper we evaluate and compare two algorithms for the calculation of KAM tori in Hamiltonian systems. The direct fitting of a torus Fourier series to a numerically integrated trajectory is the first method, while an accelerated finite Fourier transform is the second method. The finite Fourier transform, with Hanning window functions, is by far superior in both computational loading and numerical accuracy. Some thoughts on applications of KAM tori are offered.
Directory of Open Access Journals (Sweden)
Jianning Wu
2015-01-01
Full Text Available The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.
Wu, Jianning; Wu, Bin
2015-01-01
The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.
Towards the compression of parton densities through machine learning algorithms
Carrazza, Stefano
2016-01-01
One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.
Special Section on "Tools and Algorithms for the Construction and Analysis of Systems"
DEFF Research Database (Denmark)
2006-01-01
in the Lecture Notes in Computer Science series published by Springer. TACAS is a forum for researchers, developers and users interested in rigorously based tools for the construction and analysis of systems. The conference serves to bridge the gaps between different communities – including but not limited......This special section contains the revised and expanded versions of eight of the papers from the 10th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS) held in March/April 2004 in Barcelona, Spain. The conference proceedings appeared as volume 2988...... to those devoted to formal methods, software and hardware verification, static analysis, programming languages, software engineering, real-time systems, and communications protocols – that share common interests in, and techniques for, tool development. Other more theoretical papers from the conference...
Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms
Negro Maggio, Valentina; Iocchi, Luca
2015-02-01
Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.
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 (αtexture features.
Classification of large-sized hyperspectral imagery using fast machine learning algorithms
Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira
2017-07-01
We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.
Directory of Open Access Journals (Sweden)
Mazyar Seraj
2014-10-01
Full Text Available This paper describes an experimental study of learning Dijkstra’s shortest path algorithm on mobile devices. The aim of the study is to investigate and compare the impacts of two different mobile screen user interfaces on students’ satisfaction for learning the technical subject. A mobile learning prototype was developed for learning Dijkstra’s shortest path algorithm on Apple iPhone 4 operated on iPhone operating system (iOS, and Acer Inconia Tab operated on an Android operating system. Thirty students, who are either currently studying or had previously studied Computer Networks, were recruited for the usability trial. At the end of each single session, students’ satisfaction interacting with the two mobile devices was measured using QUIS questionnaire. Although there is no significant difference in students’ satisfaction between the two different mobile screen interfaces, the subjective findings indicate that Acer Inconia Tab gained higher scores as compared to Apple iPhone 4.
Mizutani, Eiji; Demmel, James W
2003-01-01
This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).
DeepDive: Declarative Knowledge Base Construction.
De Sa, Christopher; Ratner, Alex; Ré, Christopher; Shin, Jaeho; Wang, Feiran; Wu, Sen; Zhang, Ce
2016-03-01
The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.
Wahyudin; Riza, L. S.; Putro, B. L.
2018-05-01
E-learning as a learning activity conducted online by the students with the usual tools is favoured by students. The use of computer media in learning provides benefits that are not owned by other learning media that is the ability of computers to interact individually with students. But the weakness of many learning media is to assume that all students have a uniform ability, when in reality this is not the case. The concept of Intelligent Tutorial System (ITS) combined with cyberblog application can overcome the weaknesses in neglecting diversity. An Intelligent Tutorial System-based Cyberblog application (ITS) is a web-based interactive application program that implements artificial intelligence which can be used as a learning and evaluation media in the learning process. The use of ITS-based Cyberblog in learning is one of the alternative learning media that is interesting and able to help students in measuring ability in understanding the material. This research will be associated with the improvement of logical thinking ability (logical thinking) of students, especially in algorithm subjects.
Sung, Han-Yu; Hwang, Gwo-Jen
2018-01-01
Researchers have recognized the potential of educational computer games in improving students' learning engagement and outcomes; however, facilitating effective learning behaviors during the gaming process remains an important and challenging issue. In this paper, a collaborative knowledge construction strategy was incorporated into an educational…
Evaluation of a Didactic Method for the Active Learning of Greedy Algorithms
Esteban-Sánchez, Natalia; Pizarro, Celeste; Velázquez-Iturbide, J. Ángel
2014-01-01
An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of…
A generic algorithm for constructing hierarchical representations of geometric objects
International Nuclear Information System (INIS)
Xavier, P.G.
1995-01-01
For a number of years, robotics researchers have exploited hierarchical representations of geometrical objects and scenes in motion-planning, collision-avoidance, and simulation. However, few general techniques exist for automatically constructing them. We present a generic, bottom-up algorithm that uses a heuristic clustering technique to produced balanced, coherent hierarchies. Its worst-case running time is O(N 2 logN), but for non-pathological cases it is O(NlogN), where N is the number of input primitives. We have completed a preliminary C++ implementation for input collections of 3D convex polygons and 3D convex polyhedra and conducted simple experiments with scenes of up to 12,000 polygons, which take only a few minutes to process. We present examples using spheres and convex hulls as hierarchy primitives
Approximated Function Based Spectral Gradient Algorithm for Sparse Signal Recovery
Directory of Open Access Journals (Sweden)
Weifeng Wang
2014-02-01
Full Text Available Numerical algorithms for the l0-norm regularized non-smooth non-convex minimization problems have recently became a topic of great interest within signal processing, compressive sensing, statistics, and machine learning. Nevertheless, the l0-norm makes the problem combinatorial and generally computationally intractable. In this paper, we construct a new surrogate function to approximate l0-norm regularization, and subsequently make the discrete optimization problem continuous and smooth. Then we use the well-known spectral gradient algorithm to solve the resulting smooth optimization problem. Experiments are provided which illustrate this method is very promising.
Ehteshami Bejnordi, Babak; Veta, Mitko; Johannes van Diest, Paul; van Ginneken, Bram; Karssemeijer, Nico; Litjens, Geert; van der Laak, Jeroen A W M; Hermsen, Meyke; Manson, Quirine F; Balkenhol, Maschenka; Geessink, Oscar; Stathonikos, Nikolaos; van Dijk, Marcory Crf; Bult, Peter; Beca, Francisco; Beck, Andrew H; Wang, Dayong; Khosla, Aditya; Gargeya, Rishab; Irshad, Humayun; Zhong, Aoxiao; Dou, Qi; Li, Quanzheng; Chen, Hao; Lin, Huang-Jing; Heng, Pheng-Ann; Haß, Christian; Bruni, Elia; Wong, Quincy; Halici, Ugur; Öner, Mustafa Ümit; Cetin-Atalay, Rengul; Berseth, Matt; Khvatkov, Vitali; Vylegzhanin, Alexei; Kraus, Oren; Shaban, Muhammad; Rajpoot, Nasir; Awan, Ruqayya; Sirinukunwattana, Korsuk; Qaiser, Talha; Tsang, Yee-Wah; Tellez, David; Annuscheit, Jonas; Hufnagl, Peter; Valkonen, Mira; Kartasalo, Kimmo; Latonen, Leena; Ruusuvuori, Pekka; Liimatainen, Kaisa; Albarqouni, Shadi; Mungal, Bharti; George, Ami; Demirci, Stefanie; Navab, Nassir; Watanabe, Seiryo; Seno, Shigeto; Takenaka, Yoichi; Matsuda, Hideo; Ahmady Phoulady, Hady; Kovalev, Vassili; Kalinovsky, Alexander; Liauchuk, Vitali; Bueno, Gloria; Fernandez-Carrobles, M Milagro; Serrano, Ismael; Deniz, Oscar; Racoceanu, Daniel; Venâncio, Rui
2017-12-12
Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image
International Nuclear Information System (INIS)
Matsumoto, S.; Ohno, Y.; Takenaka, D.; Sugimura, K.; Yamagata, H.
2007-01-01
Classification of the nodule candidates in computer-aided detection (CAD) of lung nodules in CT images was addressed by constructing a nonlinear discriminant function using a kernel-based learning algorithm called the kernel recursive least-squares (KRLS) algorithm. Using the nodule candidates derived from the processing by a CAD scheme of 100 CT datasets containing 253 non-calcified nodules or 3 mm or larger as determined by the consensus of two thoracic radiologists, the following trial were carried out 100 times: by randomly selecting 50 datasets for training, a nonlinear discriminant function was obtained using the nodule candidates in the training datasets and tested with the remaining candidates; for comparison, a rule-based classification was tested in a similar manner. At the number of false positives per case of about 5, the nonlinear classification method showed an improved sensitivity of 80% (mean over the 100 trials) compared with 74% of the rule-based method. (orig.)
A regularized approach for geodesic-based semisupervised multimanifold learning.
Fan, Mingyu; Zhang, Xiaoqin; Lin, Zhouchen; Zhang, Zhongfei; Bao, Hujun
2014-05-01
Geodesic distance, as an essential measurement for data dissimilarity, has been successfully used in manifold learning. However, most geodesic distance-based manifold learning algorithms have two limitations when applied to classification: 1) class information is rarely used in computing the geodesic distances between data points on manifolds and 2) little attention has been paid to building an explicit dimension reduction mapping for extracting the discriminative information hidden in the geodesic distances. In this paper, we regard geodesic distance as a kind of kernel, which maps data from linearly inseparable space to linear separable distance space. In doing this, a new semisupervised manifold learning algorithm, namely regularized geodesic feature learning algorithm, is proposed. The method consists of three techniques: a semisupervised graph construction method, replacement of original data points with feature vectors which are built by geodesic distances, and a new semisupervised dimension reduction method for feature vectors. Experiments on the MNIST, USPS handwritten digit data sets, MIT CBCL face versus nonface data set, and an intelligent traffic data set show the effectiveness of the proposed algorithm.
Digital and discrete geometry theory and algorithms
Chen, Li
2014-01-01
This book provides comprehensive coverage of the modern methods for geometric problems in the computing sciences. It also covers concurrent topics in data sciences including geometric processing, manifold learning, Google search, cloud data, and R-tree for wireless networks and BigData.The author investigates digital geometry and its related constructive methods in discrete geometry, offering detailed methods and algorithms. The book is divided into five sections: basic geometry; digital curves, surfaces and manifolds; discretely represented objects; geometric computation and processing; and a
Design of fuzzy learning control systems for steam generator water level control
International Nuclear Information System (INIS)
Park, Gee Yong
1996-02-01
A fuzzy learning algorithm is developed in order to construct the useful control rules and tune the membership functions in the fuzzy logic controller used for water level control of nuclear steam generator. The fuzzy logic controllers have shown to perform better than conventional controllers for ill-defined or complex processes such as nuclear steam generator. Whereas the fuzzy logic controller does not need a detailed mathematical model of a plant to be controlled, its structure is to be made on the basis of the operator's linguistic information experienced from the plant operations. It is not an easy work and also there is no systematic way to translate the operator's linguistic information into quantitative information. When the linguistic information of operators is incomplete, tuning the parameters of fuzzy controller is to be performed for better control performance. It is the time and effort consuming procedure that controller designer has to tune the structure of fuzzy logic controller for optimal performance. And if the number of control inputs is many and the rule base is constructed in multidimensional space, it is very difficult for a controller designer to tune the fuzzy controller structure. Hence, the difficulty in putting the experimental knowledge into quantitative (or numerical) data and the difficulty in tuning the rules are the major problems in designing fuzzy logic controller. In order to overcome the problems described above, a learning algorithm by gradient descent method is included in the fuzzy control system such that the membership functions are tuned and the necessary rules are created automatically for good control performance. For stable learning in gradient descent method, the optimal range of learning coefficient not to be trapped and not to provide too slow learning speed is investigated. With the optimal range of learning coefficient, the optimal value of learning coefficient is suggested and with this value, the gradient
International Nuclear Information System (INIS)
Sánchez-Oro, J.; Duarte, A.; Salcedo-Sanz, S.
2016-01-01
Highlights: • The total energy demand in Spain is estimated with a Variable Neighborhood algorithm. • Socio-economic variables are used, and one year ahead prediction horizon is considered. • Improvement of the prediction with an Extreme Learning Machine network is considered. • Experiments are carried out in real data for the case of Spain. - Abstract: Energy demand prediction is an important problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this paper, the performance of a robust hybrid approach, composed of a Variable Neighborhood Search algorithm and a new class of neural network called Extreme Learning Machine, is discussed. The Variable Neighborhood Search algorithm is focused on obtaining the most relevant features among the set of initial ones, by including an exponential prediction model. While previous approaches consider that the number of macroeconomic variables used for prediction is a parameter of the algorithm (i.e., it is fixed a priori), the proposed Variable Neighborhood Search method optimizes both: the number of variables and the best ones. After this first step of feature selection, an Extreme Learning Machine network is applied to obtain the final energy demand prediction. Experiments in a real case of energy demand estimation in Spain show the excellent performance of the proposed approach. In particular, the whole method obtains an estimation of the energy demand with an error lower than 2%, even when considering the crisis years, which are a real challenge.
Taylor, Jonathan Christopher; Fenner, John Wesley
2017-11-29
Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson's disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Classification
Wu, Junfeng; Dai, Fang; Hu, Gang; Mou, Xuanqin
2018-04-18
Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.
Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems
Directory of Open Access Journals (Sweden)
Wu Lehui
2017-01-01
Full Text Available In this paper, a discrete variant of TLBO (DTLBO is proposed for solving the traveling salesman problem (TSP. In the proposed method, an effective learner representation scheme is redefined based on the characteristics of TSP problem. Moreover, all learners are randomly divided into several sub-swarms with equal amounts of learners so as to increase the diversity of population and reduce the probability of being trapped in local optimum. In each sub-swarm, the new positions of learners in the teaching phase and the learning phase are generated by the crossover operation, the legality detection and mutation operation, and then the offspring learners are determined based on greedy selection. Finally, to verify the performance of the proposed algorithm, benchmark TSP problems are examined and the results indicate that DTLBO is effective compared with other algorithms used for TSP problems.
Machine learning based global particle indentification algorithms at LHCb experiment
Derkach, Denis; Likhomanenko, Tatiana; Rogozhnikov, Aleksei; Ratnikov, Fedor
2017-01-01
One of the most important aspects of data processing at LHC experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging CHerenkov (RICH) detector, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, several neural networks including a deep architecture and gradient boosting have been applied to data. These new approaches provide higher identification efficiencies than existing implementations for all charged particle types. It is also necessary to achieve a flat dependency between efficiencies and spectator variables such as particle momentum, in order to reduce systematic uncertainties during later stages of data analysis. For this purpose, "flat” algorithms that guarantee the flatness property for efficiencies have also been developed. This talk presents this new approach based on machine learning and its performance.
International Nuclear Information System (INIS)
Liu, Minghua; Shi, Yong; Yan, Jiashu; Yan, Yuying
2017-01-01
Highlights: • A numerical capability combining the lattice Boltzmann method with simulated annealing algorithm is developed. • Digitized representations of random porous media are constructed using limited but meaningful statistical descriptors. • Pore-scale flow and heat transfer information in random porous media is obtained by the lattice Boltzmann simulation. • The effective properties at the representative elementary volume scale are well specified using appropriate upscale averaging. - Abstract: In this article, the lattice Boltzmann (LB) method for transport phenomena is combined with the simulated annealing (SA) algorithm for digitized porous-medium construction to study flow and heat transfer in random porous media. Importantly, in contrast to previous studies which simplify porous media as arrays of regularly shaped objects or effective pore networks, the LB + SA method in this article can model statistically meaningful random porous structures in irregular morphology, and simulate pore-scale transport processes inside them. Pore-scale isothermal flow and heat conduction in a set of constructed random porous media characterized by statistical descriptors were then simulated through use of the LB + SA method. The corresponding averages over the computational volumes and the related effective transport properties were also computed based on these pore scale numerical results. Good agreement between the numerical results and theoretical predictions or experimental data on the representative elementary volume scale was found. The numerical simulations in this article demonstrate combination of the LB method with the SA algorithm is a viable and powerful numerical strategy for simulating transport phenomena in random porous media in complex geometries.
Gulshan, Varun; Peng, Lily; Coram, Marc; Stumpe, Martin C; Wu, Derek; Narayanaswamy, Arunachalam; Venugopalan, Subhashini; Widner, Kasumi; Madams, Tom; Cuadros, Jorge; Kim, Ramasamy; Raman, Rajiv; Nelson, Philip C; Mega, Jessica L; Webster, Dale R
2016-12-13
Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. Deep learning-trained algorithm. The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0
An Intervention Model of Constructive Conflict Resolution and Cooperative Learning.
Zhang, Quanwu
1994-01-01
Tests an intervention model of constructive conflict resolution (CCR) and cooperative learning in three urban high schools. Findings show that improvements in CCR increased social support and decreased victimization for the students. These changes improved student's attitudes, self-esteem, interpersonal relations, and academic achievement. (GLR)
Slow Learner Prediction Using Multi-Variate Naïve Bayes Classification Algorithm
Directory of Open Access Journals (Sweden)
Shiwani Rana
2017-01-01
Full Text Available Machine Learning is a field of computer science that learns from data by studying algorithms and their constructions. In machine learning, for specific inputs, algorithms help to make predictions. Classification is a supervised learning approach, which maps a data item into predefined classes. For predicting slow learners in an institute, a modified Naïve Bayes algorithm implemented. The implementation is carried sing Python. It takes into account a combination of likewise multi-valued attributes. A dataset of the 60 students of BE (Information Technology Third Semester for the subject of Digital Electronics of University Institute of Engineering and Technology (UIET, Panjab University (PU, Chandigarh, India is taken to carry out the simulations. The analysis is done by choosing most significant forty-eight attributes. The experimental results have shown that the modified Naïve Bayes model has outperformed the Naïve Bayes Classifier in accuracy but requires significant improvement in the terms of elapsed time. By using Modified Naïve Bayes approach, the accuracy is found out to be 71.66% whereas it is calculated 66.66% using existing Naïve Bayes model. Further, a comparison is drawn by using WEKA tool. Here, an accuracy of Naïve Bayes is obtained as 58.33 %.
Machine learning, social learning and the governance of self-driving cars.
Stilgoe, Jack
2018-02-01
Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are they learning and how are they learning?' Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. 'Self-driving' or 'autonomous' cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.
MOD-AGE - an algorithm for age-depth model construction; U-series dated speleothems case study
Hercman, H.; Pawlak, J.
2012-04-01
We present MOD-AGE - a new system for chronology construction. MOD-AGE can be used for profiles that have been dated by different methods. As input data, the system uses the following basic measurements: activities, atomic ratios or age, as well as depth measurement. Based on probability distributions describing the measurement results, MOD-AGE estimates the age~depth relation and its confidence bands. To avoid the use of difficult-to-meet assumptions, MOD-AGE uses nonparametric methods. We applied a Monte Carlo simulation to model age and depth values based on the real distribution of counted data (activities, atomic ratios, depths etc.). Several fitting methods could be applied for estimating the relationships; based on several tests, we decide to use LOESS method (locally weighted scatterplot smoothing). The stratigraphic correction procedure applied in the MOD-AGE program uses a probability calculus, which assumes that the ages of all the samples are correctly estimated. Information about the probability distribution of the samples' ages is used to estimate the most probable sequence that is concordant according to the superposition rule. MOD-AGE is presented as a tool for the chronology construction of speleothems that have been analyzed by the U-series method, and it is compared to the StalAge algorithm presented by D. Scholtz and D.L Hoffmann (2011). Scholtz, D., Hoffmann, D. L., 2011. StalAge - An algorithm designed for construction of speleothem age models. Quaternary Geochronology 6, 369-382.
Automata Learning through Counterexample Guided Abstraction Refinement
DEFF Research Database (Denmark)
Aarts, Fides; Heidarian, Faranak; Kuppens, Harco
2012-01-01
to a small set of abstract events that can be handled by automata learning tools. In this article, we show how such abstractions can be constructed fully automatically for a restricted class of extended finite state machines in which one can test for equality of data parameters, but no operations on data...... are allowed. Our approach uses counterexample-guided abstraction refinement: whenever the current abstraction is too coarse and induces nondeterministic behavior, the abstraction is refined automatically. Using Tomte, a prototype tool implementing our algorithm, we have succeeded to learn – fully......Abstraction is the key when learning behavioral models of realistic systems. Hence, in most practical applications where automata learning is used to construct models of software components, researchers manually define abstractions which, depending on the history, map a large set of concrete events...
Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong
2018-01-01
Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.
Preference learning for cognitive modeling: a case study on entertainment preferences
DEFF Research Database (Denmark)
Yannakakis, Georgios; Maragoudakis, Manolis; Hallam, John
2009-01-01
Learning from preferences, which provide means for expressing a subject's desires, constitutes an important topic in machine learning research. This paper presents a comparative study of four alternative instance preference learning algorithms (both linear and nonlinear). The case study...... investigated is to learn to predict the expressed entertainment preferences of children when playing physical games built on their personalized playing features (entertainment modeling). Two of the approaches are derived from the literature--the large-margin algorithm (LMA) and preference learning...... with Gaussian processes--while the remaining two are custom-designed approaches for the problem under investigation: meta-LMA and neuroevolution. Preference learning techniques are combined with feature set selection methods permitting the construction of effective preference models, given suitable individual...
Semi-supervised prediction of gene regulatory networks using machine learning algorithms.
Patel, Nihir; Wang, Jason T L
2015-10-01
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin
2018-02-22
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.
Hardware Acceleration of Adaptive Neural Algorithms.
Energy Technology Data Exchange (ETDEWEB)
James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-11-01
As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.
Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning
Directory of Open Access Journals (Sweden)
Xian Shan
2016-01-01
Full Text Available Bat Algorithm (BA is a swarm intelligence algorithm which has been intensively applied to solve academic and real life optimization problems. However, due to the lack of good balance between exploration and exploitation, BA sometimes fails at finding global optimum and is easily trapped into local optima. In order to overcome the premature problem and improve the local searching ability of Bat Algorithm for optimization problems, we propose an improved BA called OBMLBA. In the proposed algorithm, a modified search equation with more useful information from the search experiences is introduced to generate a candidate solution, and Lévy Flight random walk is incorporated with BA in order to avoid being trapped into local optima. Furthermore, the concept of opposition based learning (OBL is embedded to BA to enhance the diversity and convergence capability. To evaluate the performance of the proposed approach, 16 benchmark functions have been employed. The results obtained by the experiments demonstrate the effectiveness and efficiency of OBMLBA for global optimization problems. Comparisons with some other BA variants and other state-of-the-art algorithms have shown the proposed approach significantly improves the performance of BA. Performances of the proposed algorithm on large scale optimization problems and real world optimization problems are not discussed in the paper, and it will be studied in the future work.
Flowshop Scheduling Problems with a Position-Dependent Exponential Learning Effect
Directory of Open Access Journals (Sweden)
Mingbao Cheng
2013-01-01
Full Text Available We consider a permutation flowshop scheduling problem with a position-dependent exponential learning effect. The objective is to minimize the performance criteria of makespan and the total flow time. For the two-machine flow shop scheduling case, we show that Johnson’s rule is not an optimal algorithm for minimizing the makespan given the exponential learning effect. Furthermore, by using the shortest total processing times first (STPT rule, we construct the worst-case performance ratios for both criteria. Finally, a polynomial-time algorithm is proposed for special cases of the studied problem.
Geometrical Modification of Learning Vector Quantization Method for Solving Classification Problems
Directory of Open Access Journals (Sweden)
Korhan GÜNEL
2016-09-01
Full Text Available In this paper, a geometrical scheme is presented to show how to overcome an encountered problem arising from the use of generalized delta learning rule within competitive learning model. It is introduced a theoretical methodology for describing the quantization of data via rotating prototype vectors on hyper-spheres.The proposed learning algorithm is tested and verified on different multidimensional datasets including a binary class dataset and two multiclass datasets from the UCI repository, and a multiclass dataset constructed by us. The proposed method is compared with some baseline learning vector quantization variants in literature for all domains. Large number of experiments verify the performance of our proposed algorithm with acceptable accuracy and macro f1 scores.
Classification and authentication of unknown water samples using machine learning algorithms.
Kundu, Palash K; Panchariya, P C; Kundu, Madhusree
2011-07-01
This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.
2018-05-01
This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.
French, Robert M; Glady, Yannick; Thibaut, Jean-Pierre
2017-08-01
In recent years, eyetracking has begun to be used to study the dynamics of analogy making. Numerous scanpath-comparison algorithms and machine-learning techniques are available that can be applied to the raw eyetracking data. We show how scanpath-comparison algorithms, combined with multidimensional scaling and a classification algorithm, can be used to resolve an outstanding question in analogy making-namely, whether or not children's and adults' strategies in solving analogy problems are different. (They are.) We show which of these scanpath-comparison algorithms is best suited to the kinds of analogy problems that have formed the basis of much analogy-making research over the years. Furthermore, we use machine-learning classification algorithms to examine the item-to-item saccade vectors making up these scanpaths. We show which of these algorithms best predicts, from very early on in a trial, on the basis of the frequency of various item-to-item saccades, whether a child or an adult is doing the problem. This type of analysis can also be used to predict, on the basis of the item-to-item saccade dynamics in the first third of a trial, whether or not a problem will be solved correctly.
Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder.
Directory of Open Access Journals (Sweden)
Matthew J Maenner
Full Text Available The Autism and Developmental Disabilities Monitoring (ADDM Network conducts population-based surveillance of autism spectrum disorder (ASD among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children's developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods. The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria. We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive. The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD "prevalence" was 1.46% compared to the published (clinician-determined estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site.
Graham, James; Ternovskiy, Igor V.
2013-06-01
We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
We give an equivalent construction of the infinitesimal time translation operator for partial differential evolution equation in the algebraic dynamics algorithm proposed by Shun-Jin Wang and his students. Our construction involves only simple partial differentials and avoids the derivative terms of δ function which appear in the course of computation by means of Wang-Zhang operator. We prove Wang’s equivalent theorem which says that our construction and Wang-Zhang’s are equivalent. We use our construction to deal with several typical equations such as nonlinear advection equation, Burgers equation, nonlinear Schrodinger equation, KdV equation and sine-Gordon equation, and obtain at least second order approximate solutions to them. These equations include the cases of real and complex field variables and the cases of the first and the second order time derivatives.
International Nuclear Information System (INIS)
Zhou, Z.; Holland, P.M.; Igic, P.
2014-01-01
Highlights: • A novel PV emulator is constructed by using conventional solar panels with a DC power supply. • The proposed PV emulator is cost-effectiveness, relatively easy implementation. • The proposed PV emulator avoids the bandwidth problem associated with electronics PV emulators. • Indoor testing of MPPT algorithms and power converters avoids the dependency on solar irradiation. • The PV emulating system has been used for testing a P and O MPPT algorithm and a boost dc converter. - Abstract: In this paper a novel photovoltaic (PV) emulating scheme for testing maximum power point tracking (MPPT) algorithms and PV inverters has been proposed. It is constructed by the parallel connection of conventional solar panels with a DC power supply operating in current source mode. The advantages of the proposed scheme are cost-effectiveness, relatively easy implementation and indoor testing of MPPT algorithms and power converters avoiding weather and time of day dependency on solar irradiation levels. Furthermore, the proposed PV emulator avoids the bandwidth problem associated with the dc converter based PV emulating systems. Detailed circuit connection, parameters, electrical characteristics and mathematical model of the PV emulator are presented and discussed. Proposed PV emulating system has been used to test a boost DC/DC converter controlled by Perturb and Observe (P and O) MPPT algorithm. Test results confirmed the effectiveness of the proposed PV emulation system and all achieved results correspond well to the original designed values
A deep learning method for lincRNA detection using auto-encoder algorithm.
Yu, Ning; Yu, Zeng; Pan, Yi
2017-12-06
RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly
Using Personal Construct Theory to Explore Self-Image with Adolescents with Learning Disabilities
Thomas, Samantha; Butler, Richard; Hare, Dougal Julian; Green, David
2011-01-01
A young person's construct of self can be fundamental to their psychological well being (Glick 1999; Emler 2001). However limited research has been conducted in the United Kingdom to explore self-image with adolescents with learning disabilities. Previous studies have demonstrated the effective use of personal construct theory with children…
Directory of Open Access Journals (Sweden)
Xuanyu Wang
2017-12-01
Full Text Available Terrestrial latent heat flux (LE is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs across North America using three machine learning algorithms: artificial neural network (ANN; support vector machines (SVM; and, multivariate adaptive regression spline (MARS driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period 2000–2007, all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002–2004 using a spatial resolution of 0.05°, which proved to be useful for estimating the long-term LE over North America.
The Construction of an Online Competitive Game-Based Learning System for Junior High School Students
Cheng, Yuh-Ming; Kuo, Sheng-Huang; Lou, Shi-Jer; Shih, Ru-Chu
2012-01-01
The purpose of this study aimed to construct an online competitive game-based learning system by using freeware for junior high school students and to assess its effectiveness. From the learning standpoints, game mechanisms including learning points, competition mechanism, training room mechanism, questioning & answering mechanism, tips, and…
Machine learning based cloud mask algorithm driven by radiative transfer modeling
Chen, N.; Li, W.; Tanikawa, T.; Hori, M.; Shimada, R.; Stamnes, K. H.
2017-12-01
Cloud detection is a critically important first step required to derive many satellite data products. Traditional threshold based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and have difficulty over snow/ice covered areas. With the advance of computational power and machine learning techniques, we have developed a new algorithm based on a neural network classifier driven by extensive radiative transfer modeling. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over mid-latitude snow covered areas. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors.
Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia
2011-01-01
LS-Lab provides automatic support to comparison/evaluation of the Learning Object Sequences produced by different Curriculum Sequencing Algorithms. Through this framework a teacher can verify the correspondence between the behaviour of different sequencing algorithms and her pedagogical preferences. In fact the teacher can compare algorithms…
Toolkits and Libraries for Deep Learning.
Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth
2017-08-01
Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.
Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin
2018-01-01
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406
Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin
2009-10-01
Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.
Shukor, N.B.A.; Tasir, Z.; Meijden, H.A.T. van der; Harun, J.
2014-01-01
Online collaborative learning allows discussion to occur at greater depth where knowledge can be constructed remotely. However students were found to construct knowledge at low-level where they discussed by sharing and comparing opinions; those are inadequate for new knowledge creation. As such,
Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras
Energy Technology Data Exchange (ETDEWEB)
Cui, Yonggang
2018-05-07
We propose the development of proven deep learning algorithms to flag objects and events of interest in Next Generation Surveillance System (NGSS) surveillance to make IAEA image review more efficient. Video surveillance is one of the core monitoring technologies used by the IAEA Department of Safeguards when implementing safeguards at nuclear facilities worldwide. The current image review software GARS has limited automated functions, such as scene-change detection, black image detection and missing scene analysis, but struggles with highly cluttered backgrounds. A cutting-edge algorithm to be developed in this project will enable efficient and effective searches in images and video streams by identifying and tracking safeguards relevant objects and detect anomalies in their vicinity. In this project, we will develop the algorithm, test it with the IAEA surveillance cameras and data sets collected at simulated nuclear facilities at BNL and SNL, and implement it in a software program for potential integration into the IAEA’s IRAP (Integrated Review and Analysis Program).
Luo, Gang
2017-01-01
For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic. PMID:29177022
The impact of first and second language exposure on learning second language constructions
Matusevych, Yevgen; Alishahi, Afra; Backus, Albert
2017-01-01
We study how the learning of argument structure constructions in a second language (L2) is affected by two basic input properties often discussed in literature – the amount of input and the time of L2 onset. To isolate the impact of the two factors on learning, we use a computational model that
Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent
Directory of Open Access Journals (Sweden)
Zunyi Tang
2013-01-01
Full Text Available Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.
Experiments on Supervised Learning Algorithms for Text Categorization
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.
de Corte, Erik
2012-01-01
In today's learning society, education must focus on fostering adaptive competence (AC) defined as the ability to apply knowledge and skills flexibly in different contexts. In this article, four major types of learning are discussed--constructive, self-regulated, situated, and collaborative--in relation to what students must learn in order to…
Fall detection using supervised machine learning algorithms: A comparative study
Zerrouki, Nabil; Harrou, Fouzi; Houacine, Amrane; Sun, Ying
2017-01-01
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
Fall detection using supervised machine learning algorithms: A comparative study
Zerrouki, Nabil
2017-01-05
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
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Xu Yu
2018-01-01
Full Text Available Cross-domain collaborative filtering (CDCF solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR. We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.
A Project-Based Laboratory for Learning Embedded System Design with Industry Support
Lee, Chyi-Shyong; Su, Juing-Huei; Lin, Kuo-En; Chang, Jia-Hao; Lin, Gu-Hong
2010-01-01
A project-based laboratory for learning embedded system design with support from industry is presented in this paper. The aim of this laboratory is to motivate students to learn the building blocks of embedded systems and practical control algorithms by constructing a line-following robot using the quadratic interpolation technique to predict the…
Beyond Construction: Five arguments for the role and value of critique in learning science
Henderson, J. Bryan; MacPherson, Anna; Osborne, Jonathan; Wild, Andrew
2015-07-01
This paper argues that science education has overemphasized the importance of construction at the expense of critique. In doing so, it draws on two key premises-Ford's argument that the construction of knowledge requires a dialectic between construction and critique and Mercier and Sperber's theory of argumentative reasoning that critique is essential for epistemic vigilance. Five separate cases are presented which argue that the absence of critique within school science limits the opportunities for students to engage in scientific reasoning making the learning of science less effective. These five arguments incorporate research literature surrounding the nature of science, epistemology, literacy, pedagogy, and motivation. Furthermore, we draw on data collected from cognitive think-aloud interviews to show that students can, with the appropriate prompts, engage in the important epistemic activity of critique. We conclude by examining the implications for the teaching and learning of science. In essence, we argue that the undervaluing of critique within the curriculum and pedagogy of school science results in a failure to develop the analytical faculties which are the valued hall mark of the practicing scientist; a misrepresentation of the nature of science; and, more importantly, a less effective learning experience. Critique, therefore, needs to play a central role in the teaching and learning of science.
A SOCIO-COGNITIVE APPROACH TO KNOWLEDGE CONSTRUCTION THROUGH BLENDED LEARNING
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Tuba Kocaturk
2017-01-01
Full Text Available This paper results from an educational research project that was undertaken by the School of Architecture, at the University of Liverpool funded by the Higher Education Academy in UK. The research explored technology driven shifts in architectural design studio education, identified their cognitive effects on design learning and developed an innovative blended learning approach that was implemented at a masters level digital design studio. The contribution of the research and the proposed approach to the existing knowledge and practice are twofold. Firstly, it offers a new pedagogical framework which integrates social, technical and cognitive dimensions of knowledge construction. And secondly, it offers a unique operational model through the integration of both mediational and instrumental use of digital media. The proposed model provides a useful basis for the effective mobilization of next generation learning technologies which can effectively respond to the learning challenges specific to architectural design knowledge and its means of creation.
Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow
2017-01-01
Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.
Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm
Siu, Theodore; Vivar, Miguel; Shinbrot, Troy
We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions
Single-Iteration Learning Algorithm for Feed-Forward Neural Networks
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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.
Xia, Youshen; Kamel, Mohamed S
2007-06-01
Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.
Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).
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Pinar, Ali [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kolda, Tamara G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Wake Forest Univ., Winston-Salem, MA (United States); Ballard, Grey [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mahoney, Michael [Univ. of California, Berkeley, CA (United States)
2018-01-01
Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.
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David Maldavsky
2013-08-01
Full Text Available The author first exposes a complement of a previous test about convergent validity, then a construct validity test and finally an external validity test of the David Liberman algorithm. The first part of the paper focused on a complementary aspect, the differential sensitivity of the DLA 1 in an external comparison (to other methods, and 2 in an internal comparison (between two ways of using the same method, the DLA. The construct validity test exposes the concepts underlined to DLA, their operationalization and some corrections emerging from several empirical studies we carried out. The external validity test examines the possibility of using the investigation of a single case and its relation with the investigation of a more extended sample.
Construction of SPOC-based Learning Model and its Application in Linguistics Teaching
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Hua Lu
2018-02-01
Full Text Available The design of a reasonable learning model must take the new internet age into consideration. Following a contrastive study between MOOCs and SPOCs, a SPOC-based learning model is proposed in this paper. This new learning model consists of four components, the preliminary component composed of anterior analysis and course construction, the restrictive admission component for student number control, the learning procedure component which is subdivided into pre-class session, class session and post-class session, and the evaluation component which includes both online assessment and classroom assessment. This model has its advantages and is shown to be effective through the demonstration of its application in teaching linguistics to college students.
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
Liu, Feng; Li, Hao; Ren, Chao; Bo, Xiaochen; Shu, Wenjie
2016-06-22
Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.
Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm
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Jianyong Liu
2015-01-01
Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.
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A. A. Salama
2015-03-01
Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.
A ground-up construction of deep learning
CERN. Geneva
2015-01-01
I propose to give a ground up construction of deep learning as it is in it's modern state. Starting from it's beginnings in the 90's, I plan on showing the relevant (for physics) differences in optimization, construction, activation functions, initialization, and other tricks that have been accrued over the last 20 years. In addition, I plan on showing why deeper, wider basic feedforward architectures can be used. Coupling this with MaxOut layers, modern GPUs, and including both l1 and l2 forms of regularization, we have the current "state of the art" in basic feedforward networks. I plan on discussing pre-training using deep autoencoders and RBMs, and explaining why this has fallen out of favor when you have lots of labeled data. While discussing each of these points, I propose to explain why these particular characteristics are valuable for HEP. Finally, the last topic on basic feedforward networks -- interpretation. I plan on discussing latent representations of important variables (i.e., mass, pT) that ar...
A new learning algorithm for a fully connected neuro-fuzzy inference system.
Chen, C L Philip; Wang, Jing; Wang, Chi-Hsu; Chen, Long
2014-10-01
A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.
An Alternative Route to Teaching Fraction Division: Abstraction of Common Denominator Algorithm
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İsmail Özgür ZEMBAT
2015-06-01
Full Text Available From a curricular stand point, the traditional invert and multiply algorithm for division of fractions provides few affordances for linking to a rich understanding of fractions. On the other hand, an alternative algorithm, called common denominator algorithm, has many such affordances. The current study serves as an argument for shifting curriculum for fraction division from use of invert and multiply algorithm as a basis to the use of common denominator algorithm as a basis. This was accomplished with the analysis of learning of two prospective elementary teachers being an illustration of how to realize those conceptual affordances. In doing so, the article proposes an instructional sequence and details it by referring to both the (mathematical and pedagogical advantages and the disadvantages. As a result, this algorithm has a conceptual basis depending on basic operations of partitioning, unitizing, and counting, which make it accessible to learners. Also, when participants are encouraged to construct this algorithm based on their work with diagrams, common denominator algorithm formalizes the work that they do with diagrams.
An alternative route to teaching fraction division: Abstraction of common denominator algorithm
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İsmail Özgür Zembat
2015-07-01
Full Text Available From a curricular stand point, the traditional invert and multiply algorithm for division of fractions provides few affordances for linking to a rich understanding of fractions. On the other hand, an alternative algorithm, called common denominator algorithm, has many such affordances. The current study serves as an argument for shifting curriculum for fraction division from use of invert and multiply algorithm as a basis to the use of common denominator algorithm as a basis. This was accomplished with the analysis of learning of two prospective elementary teachers being an illustration of how to realize those conceptual affordances. In doing so, the article proposes an instructional sequence and details it by referring to both the (mathematical and pedagogical advantages and the disadvantages. As a result, this algorithm has a conceptual basis depending on basic operations of partitioning, unitizing, and counting, which make it accessible to learners. Also, when participants are encouraged to construct this algorithm based on their work with diagrams, common denominator algorithm formalizes the work that they do with diagrams.
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
Construction and Evaluation of Scenarios as a Learning Strategy through Modelling-Simulation
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Juan Luis Peñaloza Figueroa
2014-12-01
Full Text Available From a systemic perspective and in the context of an increasing generalization in the use of new technologies and the change in the educational paradigm -which emphasizes guided and autonomous learning-, the learning strategy should be routed to join logical reasoning and instrumental skills (software. The inclusion of new computing and communication resources to the learning process turns them into teaching tools, which makes it possible to organize the teaching and learning process in a different way. The design of these new scenarios of study has important implications to the way information is processed, to the different levels of learning (descriptive, explanatory or analytical ones and to the way knowledge is acquired and evaluated. In addition, that design takes into account the greater student-content, student-student and student-teacher interactivities, always emphasizing guided independent learning. Thus, the construction, analysis and evaluation of scenarios through models and simulation are the strategy that best suits the current learning style followed by students.
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Mazyar Seraj
2014-06-01
Full Text Available In recent years, many studies have been carried out on how to engage and support students in e-learning environments. Portable devices such as Personal Digital Assistants (PDAs, Tablet PCs, mobile phones and other mobile equipment have been used as parts of electronic learning environments to facilitate learning and teaching for both lecturers and students. However, there is still a dearth of study investigating the effects of small screen interfaces on mobile-based learning environments. This study aims to address two objectives: (i investigate lecturer and student difficulties encountered in teaching-learning process in traditional face-to-face classroom settings, and (ii to explore lecturer and student perceptions about learning the subject through mobile devices. This paper presents the results of a qualitative study using structured interviews to investigate lecturer and student experiences and perceptions on teaching and learning Dijkstra’s shortest path algorithm via mobile devices. The interview insights were then used as inputs to define user requirements for a mobile learning prototype. The findings show that the lecturers and students raised many issues about interactivity and the flexibility of effective learning applications on small screen devices, especially for a technical subject.
Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent
2014-02-01
When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most
Statistical learning problem of artificial neural network to control roofing process
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Lapidus Azariy
2017-01-01
Full Text Available Now software developed on the basis of artificial neural networks (ANN has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.
Scene recognition based on integrating active learning with dictionary learning
Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen
2018-04-01
Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.
Semantic Learning Service Personalized
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Yibo Chen
2012-02-01
Full Text Available To provide users with more suitable and personalized service, personalization is widely used in various fields. Current e-Learning systems search for learning resources using information search technology, based on the keywords that selected or inputted by the user. Due to lack of semantic analysis for keywords and exploring the user contexts, the system cannot provide a good learning experiment. In this paper, we defined the concept and characteristic of the personalized learning service, and proposed a semantic learning service personalized framework. Moreover, we made full use of semantic technology, using ontologies to represent the learning contents and user profile, mining and utilizing the friendship and membership of the social relationship to construct the user social relationship profile, and improved the collaboration filtering algorithm to recommend personalized learning resources for users. The results of the empirical evaluation show that the approach is effectiveness in augmenting recommendation.
From Genetics to Genetic Algorithms
Indian Academy of Sciences (India)
Genetic algorithms (GAs) are computational optimisation schemes with an ... The algorithms solve optimisation problems ..... Genetic Algorithms in Search, Optimisation and Machine. Learning, Addison-Wesley Publishing Company, Inc. 1989.
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Marco Scutari
2017-03-01
Full Text Available It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimization theory, which can be adapted to the task by using the network score as the objective function to maximize. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimization in widespread use, backtracking, leverages the symmetries implied by the definitions of neighborhood and Markov blanket. In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelize constraint-based structure learning algorithms (also implemented in bnlearn and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.
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Zena M Hira
Full Text Available Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA which assumes a multivariate Gaussian model of the data. More recently, non-linear methods have been investigated. Among these, manifold learning algorithms, for example Isomap, aim to project the data from a higher dimensional space onto a lower dimension one. We have proposed a priori manifold learning for finding a manifold in which a representative set of microarray data is fused with relevant data taken from the KEGG pathway database. Once the manifold has been constructed the raw microarray data is projected onto it and clustering and classification can take place. In contrast to earlier fusion based methods, the prior knowledge from the KEGG databases is not used in, and does not bias the classification process--it merely acts as an aid to find the best space in which to search the data. In our experiments we have found that using our new manifold method gives better classification results than using either PCA or conventional Isomap.
Quantum algorithms and learning theory
Arunachalam, S.
2018-01-01
This thesis studies strengths and weaknesses of quantum computers. In the first part we present three contributions to quantum algorithms. 1) consider a search space of N elements. One of these elements is "marked" and our goal is to find this. We describe a quantum algorithm to solve this problem
Learning and Collective Knowledge Construction With Social Media: A Process-Oriented Perspective
Kimmerle, Joachim; Moskaliuk, Johannes; Oeberst, Aileen; Cress, Ulrike
2015-01-01
Social media are increasingly being used for educational purposes. The first part of this article briefly reviews literature that reports on educational applications of social media tools. The second part discusses theories that may provide a basis for analyzing the processes that are relevant for individual learning and collective knowledge construction. We argue that a systems-theoretical constructivist approach is appropriate to examine the processes of educational social media use, namely, self-organization, the internalization of information, the externalization of knowledge, and the interplay of externalization and internalization providing the basis of a co-evolution of cognitive and social systems. In the third part we present research findings that illustrate and support this systems-theoretical framework. Concluding, we discuss the implications for educational design and for future research on learning and collective knowledge construction with social media. PMID:26246643
International Nuclear Information System (INIS)
Yamaguchi, Takeshi; Yoshikawa, Hiroshi
2013-01-01
In case of teaching optical system construction, it is difficult to prepare the optical components for the attendance student. However the tangible learning is very important to master the optical system construction. It helps learners understand easily to use an inexpensive learning system that provides optical experiments experiences. Therefore, we propose the new education system for construction of optical setup with the augmented reality. To use the augmented reality, the proposed system can simulate the optical system construction by the direct hand control. Also, this system only requires an inexpensive web camera, printed makers and a personal computer. Since this system does not require the darkroom and the expensive optical equipments, the learners can study anytime, anywhere when they want to do. In this paper, we developed the system that can teach the optical system construction of the Denisyuk hologram and 2-step transmission type hologram. For the tangible learning and the easy understanding, the proposed system displays the CG objects of the optical components on the markers which are controlled by the learner's hands. The proposed system does not only display the CG object, but also display the light beam which is controlled by the optical components. To display the light beam that is hard to be seen directly, the learners can confirm about what is happening by the own manipulation. For the construction of optical holography setup, we arrange a laser, mirrors, a PBS (polarizing beam splitter), lenses, a polarizer, half-wave plates, spatial filters, an optical power meter and a recording plate. After the construction, proposed system can check optical setup correctly. In comparison with the learners who only read a book, the learners who use the system can construct the optical holography setup more quickly and correctly.
Rodela, R.
2014-01-01
This analysis reflects on the use of multidimensional constructs for the study of social learning in natural resource management. Insight from deliberative democracy and adult learning literature are used to ground the identified four dimensions (the moral dimension the cognitive dimension, the
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Juan Pardo
2015-04-01
Full Text Available Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma
2015-01-01
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698
Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma
2015-04-21
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
Supervised learning of tools for content-based search of image databases
Delanoy, Richard L.
1996-03-01
A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.
Optimal Control of Complex Systems Based on Improved Dual Heuristic Dynamic Programming Algorithm
Directory of Open Access Journals (Sweden)
Hui Li
2017-01-01
Full Text Available When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP technique, which is based on the BP neural network algorithm (BP-DHP, has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth. In this paper, a dual DHP technique based on Extreme Learning Machine (ELM algorithm (ELM-DHP was proposed. Through constructing three kinds of network structures, the paper gives the detailed realization process of the DHP technique in the ELM. The controller designed upon the ELM-DHP algorithm controlled a molecular distillation system with complex features, such as multivariability, strong coupling, and nonlinearity. Finally, the effectiveness of the algorithm is verified by the simulation that compares DHP and HDP algorithms based on ELM and BP neural network. The algorithm can also be applied to solve the data modeling and optimal control problems of similar complex systems.
In vitro molecular machine learning algorithm via symmetric internal loops of DNA.
Lee, Ji-Hoon; Lee, Seung Hwan; Baek, Christina; Chun, Hyosun; Ryu, Je-Hwan; Kim, Jin-Woo; Deaton, Russell; Zhang, Byoung-Tak
2017-08-01
Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules. Copyright © 2017. Published by Elsevier B.V.
Learning Markov models for stationary system behaviors
DEFF Research Database (Denmark)
Chen, Yingke; Mao, Hua; Jaeger, Manfred
2012-01-01
to a single long observation sequence, and in these situations existing automatic learning methods cannot be applied. In this paper, we adapt algorithms for learning variable order Markov chains from a single observation sequence of a target system, so that stationary system properties can be verified using......Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase, methods have recently been proposed for automatically learning accurate...... the learned model. Experiments demonstrate that system properties (formulated as stationary probabilities of LTL formulas) can be reliably identified using the learned model....
Munir; Sutarno, H.; Aisyah, N. S.
2018-05-01
This research aims to find out how the development of interactive multimedia based on auditory, intellectually, and repetition can improve student learning outcomes. This interactive multimedia is developed through 5 stages. Analysis stages include the study of literature, questionnaire, interviews and observations. The design phase is done by the database design, flowchart, storyboards and repetition algorithm material while the development phase is done by the creation of web-based framework. Presentation material is adapted to the model of learning such as auditory, intellectually, repetition. Auditory points are obtained by recording the narrative material that presented by a variety of intellectual points. Multimedia as a product is validated by material and media experts. Implementation phase conducted on grade XI-TKJ2 SMKN 1 Garut. Based on index’s gain, an increasing of student learning outcomes in this study is 0.46 which is fair due to interest of student in using interactive multimedia. While the multimedia assessment earned 84.36% which is categorized as very well.
Directory of Open Access Journals (Sweden)
Qingyang Zhang
2015-02-01
Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.
Virdi, Surinder; Virdi, Narinder Kaur
2014-01-01
Construction Mathematics is an introductory level mathematics text, written specifically for students of construction and related disciplines. Learn by tackling exercises based on real-life construction maths. Examples include: costing calculations, labour costs, cost of materials and setting out of building components. Suitable for beginners and easy to follow throughout. Learn the essential basic theory along with the practical necessities. The second edition of this popular textbook is fully updated to match new curricula, and expanded to include even more learning exercises. End of chapter exercises cover a range of theoretical as well as practical problems commonly found in construction practice, and three detailed assignments based on practical tasks give students the opportunity to apply all the knowledge they have gained. Construction Mathematics addresses all the mathematical requirements of Level 2 construction NVQs from City & Guilds/CITB and Edexcel courses, including the BTEC First Diploma in...
Quantum Google algorithm. Construction and application to complex networks
Paparo, G. D.; Müller, M.; Comellas, F.; Martin-Delgado, M. A.
2014-07-01
We review the main findings on the ranking capabilities of the recently proposed Quantum PageRank algorithm (G.D. Paparo et al., Sci. Rep. 2, 444 (2012) and G.D. Paparo et al., Sci. Rep. 3, 2773 (2013)) applied to large complex networks. The algorithm has been shown to identify unambiguously the underlying topology of the network and to be capable of clearly highlighting the structure of secondary hubs of networks. Furthermore, it can resolve the degeneracy in importance of the low-lying part of the list of rankings. Examples of applications include real-world instances from the WWW, which typically display a scale-free network structure and models of hierarchical networks. The quantum algorithm has been shown to display an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance among the nodes, as compared to the classical algorithm.
Direct Learning of Systematics-Aware Summary Statistics
CERN. Geneva
2018-01-01
Complex machine learning tools, such as deep neural networks and gradient boosting algorithms, are increasingly being used to construct powerful discriminative features for High Energy Physics analyses. These methods are typically trained with simulated or auxiliary data samples by optimising some classification or regression surrogate objective. The learned feature representations are then used to build a sample-based statistical model to perform inference (e.g. interval estimation or hypothesis testing) over a set of parameters of interest. However, the effectiveness of the mentioned approach can be reduced by the presence of known uncertainties that cause differences between training and experimental data, included in the statistical model via nuisance parameters. This work presents an end-to-end algorithm, which leverages on existing deep learning technologies but directly aims to produce inference-optimal sample-summary statistics. By including the statistical model and a differentiable approximation of ...
Guo, Zhan; Yan, Xuefeng
2018-04-01
Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.
Quick fuzzy backpropagation algorithm.
Nikov, A; Stoeva, S
2001-03-01
A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.
Directory of Open Access Journals (Sweden)
Dazhi Jiang
2015-01-01
Full Text Available At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.
Construction and Application Research of Isomap-RVM Credit Assessment Model
Directory of Open Access Journals (Sweden)
Guangrong Tong
2015-01-01
Full Text Available Credit assessment is the basis and premise of credit risk management systems. Accurate and scientific credit assessment is of great significance to the operational decisions of shareholders, corporate creditors, and management. Building a good and reliable credit assessment model is key to credit assessment. Traditional credit assessment models are constructed using the support vector machine (SVM combined with certain traditional dimensionality reduction algorithms. When constructing such a model, the dimensionality reduction algorithms are first applied to reduce the dimensions of the samples, so as to prevent the correlation of the samples’ characteristic index from being too high. Then, machine learning of the samples will be conducted using the SVM, in order to carry out classification assessment. To further improve the accuracy of credit assessment methods, this paper has introduced more cutting-edge algorithms, applied isometric feature mapping (Isomap for dimensionality reduction, and used the relevance vector machine (RVM for credit classification. It has constructed an Isomap-RVM model and used it to conduct financial analysis of China's listed companies. The empirical analysis shows that the credit assessment accuracy of the Isomap-RVM model is significantly higher than that of the Isomap-SVM model and slightly higher than that of the PCA-RVM model. It can correctly identify the credit risks of listed companies.
Shuxin, Li; Zhilong, Zhang; Biao, Li
2018-01-01
Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.
Construction and validation of a virtual learning object on intestinal elimination stoma
Directory of Open Access Journals (Sweden)
Cecílio Soares Rodrigues Braga
Full Text Available Objective.To construct and validate a virtual learning object (VLO on intestinal elimination stoma. Methods. Applied, descriptive and quantitative study. In 2014, eight stoma therapists and eight experts in computer science took part of the research. The VLO included four steps: i planning, ii construction of VLO and changes of content; iii development of dynamic, and iv conclusion and analysis. The VLO was inserted into the Moodle virtual learning environment. The ergonomic and pedagogical validation of the VLO was performed. Results. The experts appreciated the VLO satisfactorily, and scored it between good and full agreement. Conclusion. The VLO on intestinal elimination stoma is a tool that can be implemented at undergraduate programs in nursing and continuing education programs for nurses in clinical practice, contributing significantly to improve the theoretical skills necessary for the care of ostomized people safely, with quality and enabling self-care.
Independent tasks scheduling in cloud computing via improved estimation of distribution algorithm
Sun, Haisheng; Xu, Rui; Chen, Huaping
2018-04-01
To minimize makespan for scheduling independent tasks in cloud computing, an improved estimation of distribution algorithm (IEDA) is proposed to tackle the investigated problem in this paper. Considering that the problem is concerned with multi-dimensional discrete problems, an improved population-based incremental learning (PBIL) algorithm is applied, which the parameter for each component is independent with other components in PBIL. In order to improve the performance of PBIL, on the one hand, the integer encoding scheme is used and the method of probability calculation of PBIL is improved by using the task average processing time; on the other hand, an effective adaptive learning rate function that related to the number of iterations is constructed to trade off the exploration and exploitation of IEDA. In addition, both enhanced Max-Min and Min-Min algorithms are properly introduced to form two initial individuals. In the proposed IEDA, an improved genetic algorithm (IGA) is applied to generate partial initial population by evolving two initial individuals and the rest of initial individuals are generated at random. Finally, the sampling process is divided into two parts including sampling by probabilistic model and IGA respectively. The experiment results show that the proposed IEDA not only gets better solution, but also has faster convergence speed.
Prediction of Baseflow Index of Catchments using Machine Learning Algorithms
Yadav, B.; Hatfield, K.
2017-12-01
We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized
Learning-Based Precool Algorithms for Exploiting Foodstuff as Thermal Energy Reserve
DEFF Research Database (Denmark)
Vinther, Kasper; Rasmussen, Henrik; Izadi-Zamanabadi, Roozbeh
2015-01-01
Refrigeration is important to sustain high foodstuff quality and lifetime. Keeping the foodstuff within temperature thresholds in supermarkets is also important due to legislative requirements. Failure to do so can result in discarded foodstuff, a penalty fine to the shop owner, and health issues....... However, the refrigeration system might not be dimensioned to cope with hot summer days or performance degradation over time. Two learning-based algorithms are therefore proposed for thermostatically controlled loads, which precools the foodstuff in display cases in an anticipatory manner based on how...
On the best learning algorithm for web services response time prediction
DEFF Research Database (Denmark)
Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin
2013-01-01
In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....
Group Selection and Learning for a Lab-Based Construction Management Course
Solanki, Pranshoo; Kothari, Nidhi
2014-01-01
In construction industries' projects, working in groups is a normal practice. Group work in a classroom is defined as students working collaboratively in a group so that everyone can participate on a collective task. The results from literature review indicate that group work is more effective method of learning as compared to individual work.…
Using Machine Learning to Search for MSSM Higgs Bosons
Diesing, Rebecca
2016-01-01
This paper examines the performance of machine learning in the identification of Minimally Su- persymmetric Standard Model (MSSM) Higgs Bosons, and compares this performance to that of traditional cut strategies. Two boosted decision tree algorithms were tested, scikit-learn and XGBoost. These tests indicated that machine learning can perform significantly better than traditional cuts. However, since machine learning in this form cannot be directly implemented in a real MSSM Higgs analysis, this performance information was instead used to better understand the relationships between training variables. Further studies might use this information to construct an improved cut strategy.
Sugisaki, Kenji; Yamamoto, Satoru; Nakazawa, Shigeaki; Toyota, Kazuo; Sato, Kazunobu; Shiomi, Daisuke; Takui, Takeji
2016-08-18
Quantum computers are capable to efficiently perform full configuration interaction (FCI) calculations of atoms and molecules by using the quantum phase estimation (QPE) algorithm. Because the success probability of the QPE depends on the overlap between approximate and exact wave functions, efficient methods to prepare accurate initial guess wave functions enough to have sufficiently large overlap with the exact ones are highly desired. Here, we propose a quantum algorithm to construct the wave function consisting of one configuration state function, which is suitable for the initial guess wave function in QPE-based FCI calculations of open-shell molecules, based on the addition theorem of angular momentum. The proposed quantum algorithm enables us to prepare the wave function consisting of an exponential number of Slater determinants only by a polynomial number of quantum operations.
DEFF Research Database (Denmark)
Baira Ojeda, Ismael; Tolu, Silvia; Lund, Henrik Hautop
2017-01-01
Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds...... the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input...
Directory of Open Access Journals (Sweden)
Luciano Assalim
2013-05-01
Full Text Available For the successful realization of large engineering and construction projects (LECPs, a systemic organizational learning framework for institutional cooperation is critical. Due to the long project life-cycle of LECPs, this is particularly important for this kind of project. The objective of this paper is to analyze to what extent the conformity assessment of LECPs, carried out under Engineering, Procurement and Construction management (EPCm services, can be used as a tool for organizational learning and cooperation between typical stakeholders (project owners, engineering contractors, EPC contractors; subcontractors and certification bodies. The research, from which this paper emanates, was based on a case study concerning LECPs in an oil and gas company in Brazil. Based on its results, we suggest that the proposed organizational learning framework, supported by the conformity assessment rationale, constitutes an important management tool that can be disseminated in other organizational contexts where conformity assessments of LECPs take place.
Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression
Sato, Jo?o R.; Moll, Jorge; Green, Sophie; Deakin, John F.W.; Thomaz, Carlos E.; Zahn, Roland
2015-01-01
Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the hi...
Institute of Scientific and Technical Information of China (English)
Concha Bielza; Juan A.Fernández del Pozo; Pedro Larra(n)aga
2013-01-01
Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation.There are two main approaches to parameter setting:parameter tuning and parameter control.In this paper,we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation.The nodes of this Bayesian network are genetic algorithm parameters to be controlled.Its structure captures probabilistic conditional (in)dependence relationships between the parameters.They are learned from the best individuals,i.e.,the best configurations of the genetic algorithm.Individuals are evaluated by running the genetic algorithm for the respective parameter configuration.Since all these runs are time-consuming tasks,each genetic algorithm uses a small-sized population and is stopped before convergence.In this way promising individuals should not be lost.Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time.Moreover,our approach can cope with as yet unsolved high-dimensional problems.
Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin
2018-02-01
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
Chinese handwriting recognition an algorithmic perspective
Su, Tonghua
2013-01-01
This book provides an algorithmic perspective on the recent development of Chinese handwriting recognition. Two technically sound strategies, the segmentation-free and integrated segmentation-recognition strategy, are investigated and algorithms that have worked well in practice are primarily focused on. Baseline systems are initially presented for these strategies and are subsequently expanded on and incrementally improved. The sophisticated algorithms covered include: 1) string sample expansion algorithms which synthesize string samples from isolated characters or distort realistic string samples; 2) enhanced feature representation algorithms, e.g. enhanced four-plane features and Delta features; 3) novel learning algorithms, such as Perceptron learning with dynamic margin, MPE training and distributed training; and lastly 4) ensemble algorithms, that is, combining the two strategies using both parallel structure and serial structure. All the while, the book moves from basic to advanced algorithms, helping ...
Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.
Fan, Jianping; Zhou, Ning; Peng, Jinye; Gao, Ling
2015-11-01
In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.
Hatipoglu, Nuh; Bilgin, Gokhan
2017-10-01
In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.
Machine learning spatial geometry from entanglement features
You, Yi-Zhuang; Yang, Zhao; Qi, Xiao-Liang
2018-02-01
Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on a 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT2 point).