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检索条件"机构=Key Lab. In Machine Learning and Computational Intelligence"

共 91 条记录，以下是61-70 订阅

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TWO-PLY ITERATIVE DEEPENING IN CHINESE-CHESS COMPUTER GAME

TWO-PLY ITERATIVE DEEPENING IN CHINESE-CHESS COMPUTER GAME

引用

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 NEW HEURISTIC OF THE DECISION TREE INDUCTION

A NEW HEURISTIC OF THE DECISION TREE INDUCTION

引用

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

作者： NING LI LI ZHAO AI-XIA CHEN QING-WU MENG GUO-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

Decision tree induction is one of the useful approaches for extracting classification knowledge from a set of feature-based instances. The most popular heuristic information used in the decision tree generation is the minimum entropy. This heuristic information has a serious disadvantage-the poor generalization capability [3]. Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. It has good generalization. Considering the relationship between the classification margin of support vector machine(SVM) and the generalization capability, the large margin of SVM can be used as the heuristic information of decision tree, in order to improve its generalization *** paper proposes a decision tree induction algorithm based on large margin heuristic. Comparing with the binary decision tree using the minimum entropy as the heuristic information, the experiments show that the generalization capability has been improved by using the new heuristic.

关键词： Decision tree Support vector machines SMO Clustering large margin Generalization

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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...

引用

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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A NEW ALGORITHM FOR SOLVING CONVEX HULL PROBLEM AND ITS APPLICATION TO FEATURE SELECTION

A NEW ALGORITHM FOR SOLVING CONVEX HULL PROBLEM AND ITS APPL...

引用

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

作者： FENG GUO XI-ZHAO WANG YAN LI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

A new method to solve the convex hull problem in n-dimensional spaces is proposed in this paper. At each step, a new point is added into the convex hull if the point is judged to be out of the current convex hull by a linear programming model. For the linear separable classification problem, if an instance is regarded as a point of the instances space, the overlap does not still occur between the convex hulls of different classes after a feature is deleted, then we can delete that feature. Repeat this process, an algorithm for feature selection is given. Experimental results show the effectiveness of the algorithm.

关键词： Convez Hull Linear Programming Problem Feature Selection

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NRMCS: NOISE REMOVING BASED ON THE MCS

NRMCS: NOISE REMOVING BASED ON THE MCS

引用

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

作者： XI-ZHAO WANG BO WU YU-LIN HE XIANG-HAO PEI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative samples is proposed. Compared with other algorithms which remove the noise by Wilson Editing in advance for the representative samples selection, this algorithm performs the processes of noise removing and samples selection simultaneously. According to this method, most noise can be deleted and the most representative samples can be identified and retained. The experiments show that the proposed method can greatly remove the redundant samples and noise as well as increase the accuracy of solutions when it is used for classification tasks.

关键词： MCS Sample Selection ICF Representative Subset Noise Wilson Editing

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A new instance selection algorithm based on contribution for nearest neighbour classification

A new instance selection algorithm based on contribution for...

引用

International Conference on machine learning and Cybernetics

作者： Cai, Yong-Hua Wu, Bo He, Yu-Lin Zhang, Ye Department of Mathematics and Computer Science Hebei Normal University for Nationalities Chengde067000 Hebei China Automation Department Chengde Iron and Steel Branch Hebei Iron and Steel Group Co. Ltd. Chengde 067000 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Nearest Neighbor Classifier is one of the most classical lazy learning schemes. The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow with a large database. In this paper, a new Instances Selection algorithm based on Classification Contribution Function shortly named ISCC is presented. In this algorithm, a function is introduced to evaluate the classification ability of the instances. For each instance, the function considers its contribution to the neighbor instances not only with same class but also with different class. Then an instance with the highest value of Classification Contribution Function is added to the condensed subset and the instances which can be classified correctly are deleted in each iteration. This process is repeated until the subset is no longer getting larger. The time complexity of ISCC is Ο (in2). The experimental results on two artificial databases and some real databases demonstrate the effectiveness and the feasibility of the proposed algorithm. Compared to traditional methods, such as MCS, ICF and ENN, the condensed sets obtained by ISCC is superior in storage and classification accuracy. © 2010 IEEE.

关键词： Classification (of information)

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AN INSTANCE SELECTION ALGORITHM BASED ON CONTRIBUTION

AN INSTANCE SELECTION ALGORITHM BASED ON CONTRIBUTION

引用

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

作者： XI-ZHAO WANG NING ZHANG TAO XIAO Key Lab.for Machine Learning and Computational Intelligence College of Mathematics and Computer Sci College of Science Agricultural University of Hebei Baoding 071002 China

This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.

关键词： Instance selection nearest neighbor rule condensed set MCS FCNN

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A DYNAMIC FUZZY MEASURE FOR MULTIPLE CLASSIFIER FUSION

A DYNAMIC FUZZY MEASURE FOR MULTIPLE CLASSIFIER FUSION

引用

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

作者： YA-JING ZHANG XUE-FEI LI JUN-FEN CHEN HUI-MIN FENG College of Science Agricultural University of Hebei Baoding 071001 China Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie

It has been shown that the fuzzy integral is an effective tool for the fusion of multiple classifiers. Of primary importance in the development of the system is the choice of the measure which embodies the importance of subsets of classifiers. In this paper we propose a method for a dynamic fuzzy measure which will change following the pattern to be classified (data dependent). This method uses the neural network which has good study ability. Our experiment results show that this method make the classification accurate improve.

关键词： Multiple classifier Fusion Fuzzy measure Fuzzy integral Neural network

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Refining Width Parameter of Gaussian Kernel Functionfor Tuning Coherent Point Drift(CPD) Registration

Refining Width Parameter of Gaussian Kernel Functionfor Tuni...

引用

作者： Junfen CHEN Qiang HE Yan LI Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Information Sciences Hebei University School of Sciences Beijing University of Civil Engineering and Architecture

Coherent point drift(CPD), a sophistic non-rigid point sets registration method, is successfully applied in computer vision, medical image analysis, to name a few. Its registration error, however, is affected greatly by three free parameters. One of them, width parameter of Gaussian kernel function, is studied in this paper to tune registration error of the CPD method. Before computing width parameter by a heuristic algorithm, the given data is regulated using minmax and standard normalization in effort to remove heterogeneity among the features of the data. Several experiments are designed on the availab.e six datasets to examine the effectiveness of CPD based on the refined width parameter. Experimental comparison indicated that the refined width parameter from the normalized data can reduce registration error of the CPD method, e.g., by 13.76% on bat dataset.

关键词： Normalization Gaussian kernel Width parameter Coherent point drift(CPD) Registration error(MSE)

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USING 2-ADDITIVE FUZZY MEASURE IN MULTIPLE CLASSIFIER SYSTEM

USING 2-ADDITIVE FUZZY MEASURE IN MULTIPLE CLASSIFIER SYSTEM

引用

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

作者： AI-XIA CHEN GUO-FANG ZHANG NING LI LI ZHAO GUO-QIANG YUAN Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Department of Basic Courses Hebei College of Finance Baoding 071051 China

Fuzzy measure and integral are widely used in Multiple Classifier System (MCS). But the number of coefficients involved in the fuzzy integral model grows exponentially with the number of classifiers to be aggregated. The main difficulty is to identify all these coefficients. This paper does an attempt Using 2-additrve fuzzy measure in Multiple Classifier System. Our conclusion is that when different interactions exist in different classifiers the complexity of the computation can be significantly reduced by 2-order additive measure.A simple example is included to illustrate the 2-order additive measure.

关键词： Fuzzy measure λ - fuzzy measure 2-additive fuzzy measure Interaction Multiple Classifier System

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