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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： QUN-FENG ZHANG YU-TING JIANG ZHI-QIANG LI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Industrial and commercial college Heibei University Baoding 071002 China Hebei Information Engineering School Baoding 071000 China

Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.

关键词： Rough Set Concept learning Sample Complezity PAC-Learnability

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Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel learning for Image Classification

Local, Mid-Level and Convolutional Features Fusion Using Mul...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Yao Lu Hui Zhang Bojun Xie Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.

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Unsupervised feature selection based on feature relevance

Unsupervised feature selection based on feature relevance

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Feng Zhang Ya-Jun Zhao Jun-Fen Key Lab. of Machine Learning and computational Intelligence Hebei University Baoding China College of Physics Science and Technology Hebei University Baoding China Key Lab. of Machine Learning and computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

Feature selection is an essential technique used in data mining and machine learning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems. Firstly, the original features are clustered according to their relevance degree defined by mutual information. And then the most informative feature is selected from each cluster based on the contribution-information of each feature. The experimental results show that the proposed method can match some popular supervised feature selection methods. And the features selected by our method do include most of the information hidden in the overall original features.

关键词： machine learning Mutual information Random variables Entropy Unsupervised learning Information theory Cybernetics Educational institutions Data mining computational intelligence

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An improved differential evolution and its application to determining feature weights in similarity based clustering

An improved differential evolution and its application to de...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Ru Dong Daniel S. Yeung Xi-Zhao Wang Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding China Machine Learning and Cybernetics Research Center South China University of Technology Guangzhou China

ISBN: (纸本)9781479902590

Feature weighting, which is considered as an extension of feature selection techniques, has been successfully applied to improve the performance of clustering. Focusing on the clustering based on a similarity matrix, we design an optimization model to minimize the fuzziness of similarity matrix by learning feature weights. The objective of this model is to get a more reasonable result of clustering through minimizing the uncertainty (fuzziness and non-specificity) of similarity matrix. To solving this optimization model effectively, we propose a new searching approach which integrates together multiple evolution strategies of both differential evolution and dynamic differential evolution. The experimental results on several benchmark datasets show that the performance of the proposed method is significantly improved compared to that of gradient-descent-based approach in terms of five selected clustering evaluation indices, i.e., fuzziness of similarity matrix, intra-class similarity, inter-class similarity, ratio of intra-class similarity to inter-class similarity.

关键词： Abstracts Noise measurement

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TWO-PLY ITERATIVE DEEPENING IN CHINESE-CHESS COMPUTER GAME

TWO-PLY ITERATIVE DEEPENING IN CHINESE-CHESS COMPUTER GAME

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2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： XI-ZHAO WANG YU-LIN HE PAN SU WEN-LIANG LI Key Lab.for Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

In Chinese-chess computer game (CCCG), a computer player could find the best move for a given board position by using alpha-beta search algorithm. The technique of iterative deepening is an enhancement to alpha-beta search. It is helpful to reduce the size of game tree. In this paper, we improved the prototypical one-ply iterative deepening (OPID) and proposed two-ply iterative deepening (TPID). In game tree searching, we extend the search by two plies from the previous iteration. An iterated series of 2-ply, 4-ply, 6-ply,…searches is carried out. In the experiments, we validate that TPID is feasible and effective. Through applying TPID to minimax search and alpha-beta search respectively, we found that the total number of nodes generated in TPID minimax search and TPID alpha-beta search are all reduced compared with OPID.

关键词： Chinese Chess Computer Game Game Tree One-Ply Iterative Deepening Two-Ply Iterative Deepening Minimaz search Alpha-beta search algorithm

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A PSO-GD-BASED HYBRID ALGORITHM FOR GENERAL FUZZY MEASURE DETERMINATION

A PSO-GD-BASED HYBRID ALGORITHM FOR GENERAL FUZZY MEASURE DE...

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2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： HUAN-YU ZHAO XI-ZHAO WANG Key Lab.for Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called CDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

关键词： Fuzzy measure Fuzzy integral Particle swarm optimization Gradient descent algorithm

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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A text classification method based on the improved CR-tree and the weighted association rule

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Journal of computational Information Systems 2009年第6期5卷 1577-1584页

作者： Wang, Yu Zhao, Yao Yuan, Fang College of Mathematics and Computer Science Hebei University Baoding 071002 China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding 071002 China

Classification based on association rules is a common and easily understand algorithm for text classification. To improve its classification accuracy, the key is to generate more effective rules. Sometimes, it will overdraw the role of some training texts. To avoiding this and generate more effective rules, this paper defines the Classification Path and proposes a new text classifier (CP-tree) based on CR-tree. When a new text is coming, to avoid overdrawing, association rules are generated through scanning Classification Path;in addition, to make the role of association rules in class prediction more reasonable, the association rules and weight for the new text are obtained not only according to the training texts, but also the new texts. Experimental results show that the accuracy of text classification is improved with the algorithm based on CP-tree. 1553-9105/ Copyright © 2009 Binary Information Press.

关键词： Classification (of information)

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Support vector machine for suppressing error method

Support vector machine for suppressing error method

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1st International Conference on Information Science and Engineering, ICISE2009

作者： Lu, Shuxia Shi, Pu Chen, Ming Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Baoding Pascali Cable TV Integrater Information Network Co. Ltd. Baoding China College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9780769538877

Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of [0, 1]. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.

关键词： Support vector machines

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Markov property of Markov chains and its test

Markov property of Markov chains and its test

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yu-Fen Zhang Qun-Feng Zhang Rui-Hua Yu Key Lab. for Machine Learning and Computational Intelligence Hebei University Baoding China Department of Information Technology Baoding University Baoding China

Markov chains, with Markov property as its essence, are widely used in the fields such as information theory, automatic control, communication techniques, genetics, computer sciences, economic administration, education administration, and market forecasts. While using Markov chains to predict the future events, we must test the Markov property of random variable sequences of the past statistic data. Only when the random variable sequences satisfy the Markov property, can the prediction could be precise. This paper discusses the concept of Markov property and its features, studies its test method, and by example demonstrates the effectiveness of this prediction method.

关键词： Markov processes Random variables machine learning Biological system modeling Prediction methods Water resources

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