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KM-A∗ pathfinding algorithm based on hierarchical clustering and strengthened DB Index criteria

KM-A∗ pathfinding algorithm based on hierarchical clusterin...

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Cai Chen Yan Li Tie-Song Li Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

In computer games, high-quality pathfinding algorithms are important to bring satisfactory experiences to the players, which may improve the playability of computer game. The method of KM-A* belongs to hierarchical pathfinding, which incorporates the information of obstacle distribution when partitioning game maps using K-means clustering. This algorithm reduced unnecessary storage and unnatural paths in some open areas. However, the found path by KM-A* is very unstable and the quality closely depends on the clustering results. In this paper, we proposed an improved KM-A* algorithm, which aims to enhance the stability and quality of pathfinding. First, an agglomerative hierarchical clustering algorithm is used to determine the initial cluster centers, and K-Means algorithm is then applied to cluster obstacle nodes with k values which can possibly generate best cluster categories on game maps. Secondly, the strengthened form of DB Index criteria is defined by adding impact factors to the standard DB Index criteria to measure the quality of clustering results. Finally the best clustering is selected based on this new DB Index, which will provide a guidance to further generate an abstract map for on-line pathfinding. Experiments are conducted to compare the performance of KM-A* and improved KM-A*, which show that the latter can find more stable paths and use less on-line processing time.

关键词： Indexes Clustering algorithms Games Partitioning algorithms machine learning Algorithm design and analysis Heuristic algorithms

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L1-Norm-Based 2DLPP

L1-Norm-Based 2DLPP

引用

2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Hao-Xin Zhao Hong-Jie Xing Xi-Zhao Wang Jun-Fen Chen Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

In this paper, we propose a new L1-Norm-Based two-dimensional locality preserving projections (2DLPP-L1). Traditional 2D-LPP can preserve local structure and extract feature directly form matrices, which shows great advantages. However, it is based on L2 norm. It is well known that L2-norm-based criterion is sensitive to outliers. We generalize 2D-LPP to its corresponding L1-norm-based version, i.e. 2DLPP-L1, which is more robust against outliers. To evaluate the performance of 2DLPP-L1, several experiments are performed on the ORL face databases. Experimental results demonstrate that 2DLPP-L1 has better performance than its related methods.

关键词： L1 norm 2DLPP outliers two dimensional projections

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Regional objects based image retrieval

Regional objects based image retrieval

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2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Jian-Guo Wu Xi-Zhao Wang Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

Content-based image retrieval has become an important research area. In order to extract the semantic information within the user’s query concept, we propose an image retrieval method based on regional objects. It is regarded as the pre-processing of a given query image, that is to say, when we get a query image, it needs us to segment the regional object which is useful or interesting, and retrieve according to the segmented fragment. Moreover, we propose a correlation coefficient based color representation. Experimental results demonstrate that our proposed approach performs much better than its related methods. Furthermore, the presented system has a high retrieval precision and keeps color consistency between the similarity images.

关键词： Content-based image retrieval Regional objects Pre-processing Correlation coefficient

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A survey of the initialization of centers and widths in radial basis function network for classification

A survey of the initialization of centers and widths in radi...

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Ru Dong Patrick P. K. Chan Wing W. Y. Ng Daniel S. Yeung Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou China

The radial basis function network (RBFN) has been widely used in various fields such as function regression, pattern recognition, and error detection, etc. However, the structural parameters of RBFN including the number of hidden units, centers vectors, and widths (variances) are one of the most important issues when training a RBFN, which greatly affect the performance of RBFN. So, the objective of this paper is to construct an elementary survey about this problem. Firstly, the fundamental knowledge and notations of RBFN is introduced. Secondly, we summarize most existing network structure initialization methods for RBFN and categorize them into four goups. Then some typical appraoches for each category are introduced and discussed. The disadvantages and virtues for parts of methods are also introduced. Finally, the paper is concluded with a discussion of current difficulties and possible future directions about RBFN architecture selection.

关键词： Training Neurons Artificial neural networks Clustering algorithms Optimization machine learning

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PSO-based method for learning similarity measure of nominal features

PSO-based method for learning similarity measure of nominal ...

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International Conference on machine learning and Cybernetics

作者： Li, Yan Zhang, Xiu-Li Key Lab. in Machine Learning and Computational Intelligence Faculty of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781424465262

This paper presents a PSO-based method for learning similarity measure of nominal features for case based reasoning classifiers (i.e. CBR classifiers). The symbolic features considered here takes completely unordered values. It has been indicated in [3] that in specific classification task, the similarities between these nominal feature values can not be simply considered as either 0 or 1. A GA-based approach has been developed for learning similarity measure of such feature values. However, when the number of features and feature values become larger, the GA-based algorithm's convergence speed obviously slows down, and the accuracy of classification may be also affected. To address this problem, we propose a PSO-based algorithm for learning similarity measure of nominal features and further describe feature importance through the learned similarity measure. The experimental results show that, using the proposed PSO-based algorithm, the convergence speed is much faster than that of GA-based algorithm and the accuracy is also improved. In addition, we also explain that the feature importance defined through the learned similarities is essentially consistent with that in rough sets, and an illustrative example is finally provided. © 2010 IEEE.

关键词： Particle swarm optimization (PSO)

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Reductions in inconsistent decision systems based on dominance relations

Reductions in inconsistent decision systems based on dominan...

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International Conference on machine learning and Cybernetics

作者： Li, Yan Sun, Na-Xin Zhao, Jin Key Lab. In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781424465262

By incorporating domination principle in inconsistent decision systems based on dominance relations, we define the concept of distribution function for a decision system to directly reflect the inconsistent degree of this system. A new type of distribution reduction and maximum distribution reduction are correspondingly introduced. The relationships between these two reductions are discussed, and their judgment theorems are given. In addition, this article also discusses the relations of several existing reductions, including compatible reduction, absolute reduction, and the proposed distribution reduction and maximum distribution reduction. © 2010 IEEE.

关键词： Distribution functions

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Chinese keyword search over relational databases

Chinese keyword search over relational databases

引用

World Congress on Software Engineering

作者： Zhu, Liang Zhu, Yong Ma, Qin Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China Department of Foreign Language Teaching and Research Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9780769543031

Based on a knowledge base, we propose a new method to realize free-style Chinese keyword search over relational databases. Firstly, an index (also called knowledge base) is built by extracting related information of Chinese tuple words in a database, then query words and tuple words are matched quickly each other by with the index to obtain some of candidate answers for a given query. Secondly, we present a new ranking strategy to compute similarities between the query and the candidate answers, and refine the candidate answers by a matching algorithm proposed in this paper. Finally, we rank refined candidate answers to obtain top-N results. The experiments are conducted using a real dataset, and the experimental results show that our method is efficient and effective. © 2010 IEEE.

关键词： Search engines

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Layout identification of printed mathematical formula for recognition

Layout identification of printed mathematical formula for re...

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International Conference on Information Engineering and Computer Science

作者： Tian, Xue-Dong Zhao, Yan Wang, Hui Wang, Qiang-Jun College of Mathematics and Computer Science Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China Baoding Senior Technical College Baoding China College of Literature Hebei University Baoding China

ISBN: (纸本)9781424479412

Printed mathematical formulas edited by different soft wares have some obvious differences. To distinguish it before recognition is beneficial to the formula recognition. Based on the statistical analysis to the characteristics of Latex and Word typesetting, an algorithm is designed to identify Latex and Word layout of printed formulas. The experiment indicates that this algorithm can achieve a relative high accuracy to standard printed documents. ©2010 IEEE.

关键词： Latexes

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A HSC-based sample selection method for Support Vector machine

A HSC-based sample selection method for Support Vector Machi...

引用

International Conference on machine learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 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

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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Data Extraction Based on Index Path in Web

Data Extraction Based on Index Path in Web

引用

International Workshop on Education Technology and Computer Science, ETCS

作者： Ya Gao Fang Yuan Ming Zhang Key Laboratory in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9781424463886;9780769539874

Data extraction in Web is to obtain the desired information to users in Web pages. For a more accurately valuable data extraction, this paper proposes a new method called data extraction based on index path in Web (DEIP) . This approach establishes the index path for each text node using XML DOM; defines the prefix of data-rich by keywords in the index path; generate extraction rule and obtain a wrapper according. The wrapper can extract data automatically in the same domain from a Website. It does relevant to the continuity, the structural similarity, and the location relations of the useful information in Web pages, but not the HTML tag, Experiments indicate that this method is efficient in the recall and the precision of data extraction.

关键词： Data mining Web pages HTML Internet Databases Computer science XML Web search Search engines Web page design

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