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Potential support vector machine based on the reduced samples

Potential support vector machine based on the reduced sample...

引用

International Conference on Information Science and Engineering

作者： Lu, Shu-Xia Cao, Gui-En Meng, Jie Wang, Hua-Chao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9781424480968

When the training dataset is very large, the learning process of potential support vector machine takes up so large memory that the training speed is very slow. To accelerate the training speed of the potential support vector machine (PSVM) for large-scale datasets, a new method is proposed, which introduces PSVM based on the reduced samples. The new method removes most non-support vectors, and keeps the samples on and near the boundary, which may be the support vectors, as the new training samples. This method is more suitable to large-scale datasets. The experimental results show that the proposed method performs well to decrease the consumption of computer memory,and accelerate the training speed of PSVM. © 2010 IEEE.

关键词： Support vector machines

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An improved cluster oriented fuzzy decision trees 1

引用

12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009

作者： Su, Shan Wang, Xizhao Zhai, Junhai Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (数字)9783642106460

ISBN: (纸本)3642106455

In this paper, an improved cluster oriented decision trees algorithm shortly named ICFDT is presented. In this algorithm, fuzzy C-means clustering algorithm (FCM) without instance labels is used to split the nodes and two novel node expanding criteria are proposed. One criterion uses the ratio of homogenous samples in the node to split;the other splits the node by membership degree without labels. The experimental results in artificial and machine learning datasets show that our method can achieve better performance comparing to standard decision tree named C4.5. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Decision trees

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The application of decision tree in Chinese email classification

The application of decision tree in Chinese email classifica...

引用

International Conference on machine learning and Cybernetics

作者： Chen, Hao Zhan, Yan Li, Yan Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781424465262

Email is a kind of semi-structured document, some important attributes are contained in its structure, and especially using spam-specific features could improve the email classification results. In this paper, we apply decision tree data mining technique to dig out the potential association rules among these attributes of email, and then to identify unknown email's category based on these rules. According to the experiment of applying numerous Chinese emails to our email classifier, the efficiency of our method is not lower than that of other existing methods of checking whole email content text. Meanwhile our method can reduce the cost of computation and consumption of system resources. © 2010 IEEE.

关键词： Association rules

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

KM-A* pathfinding algorithm based on hierarchical clustering...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Chen, Cai Li, Yan Li, Tie-Song Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

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. © 2011 IEEE.

关键词： Computer games

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A fast support vector machine classification algorithm based on Karush-Kuhn-Tucker conditions 1

引用

12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009

作者： Zhang, Ying Wang, Xizhao Zhai, Junhai Key Lab. for Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (数字)9783642106460

ISBN: (纸本)3642106455

Although SVM have shown potential and promising performance in classification, they have been limited by speed particularly when the training data set is large. In this paper, we propose an algorithm called the fast SVM classification algorithm based on Karush-Kuhn-Tucker (KKT) conditions. In this algorithm, we remove points that are independent of support vectors firstly in the training process, and then decompose the remaining points into blocks to accelerate the next training. From the theoretical analysis, this algorithm can remarkably reduce the computation complexity and accelerate SVM training. And experiments on both artificial and real datasets demonstrate the efficiency of this algorithm. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Support vector machines

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A rank reduced matrix method in extreme learning machine

A rank reduced matrix method in extreme learning machine

引用

9th International Symposium on Neural Networks, ISNN 2012

作者： Lu, Shuxia Zhang, Guiqiang Wang, Xizhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9783642313455

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. but when dealing with large datasets, we need more hidden nodes to enhance training and testing accuracy, in this case, this algorithm can't achieve high speed any more, sometimes its training can't be executed because the bias matrix is out of memory. We focus on this issue and use the Rank Reduced Matrix (MMR) method to calculate the hidden layer output matrix, the result showed this method can not only reach much higher speed but also better improve the generalization performance whenever the number of hidden nodes is large or not. © 2012 Springer-Verlag.

关键词： Singular value decomposition

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Separation theorem and maximal margin classification for fuzzy number spaces

Separation theorem and maximal margin classification for fuz...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： He, Qiang Li, Hong-Liang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The theory of machine learning in metric space is a new research topic and has drawn much attention in recent years. The theoretical foundation of this topic is the question under which conditions two sample sets can be separated in this space. In this paper, motivated by developing a new support vector machine (SVM) in fuzzy number space, we present a necessary and sufficient condition of separating two finite classes of samples by a hyper-plane in n-dimensional fuzzy number space. We also present an attainable expression of maximal margin of the separating hyper-planes which includes some cases of the classes of infinite samples in n-dimensional fuzzy number space. These results generalize and improve the corresponding conclusions for the theory of SVM in Hilbert space to fuzzy number space. © 2011 IEEE.

关键词： Support vector machines

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Support vector machine based on a new reduced samples method

Support vector machine based on a new reduced samples method

引用

International Conference on machine learning and Cybernetics

作者： Lu, Shu-Xia Meng, Jie Cao, Gui-En Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781424465262

The support vectors play an important role in the training to find the optimal hyper-plane. For the problem of many non-support vectors and a few support vectors in the classification of SVM, a method to reduce the samples that may be not support vectors is proposed in this paper. First, adopt the Support Vector Domain Description to find the smallest sphere containing the most data points, and then remove the objects outside the sphere. Second, remove the edge points based on the distance of each pattern to the centers of other classes. In comparison with the standard SVM, the experimental results show that the new algorithm in the paper is capable of reducing the number of samples as well as the training time while maintaining high accuracy. © 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

2nd International Workshop on Education Technology and Computer Science, ETCS 2010

作者： Gao, Ya Yuan, Fang Zhang, Ming Key Lab. in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University BaodingHebei China

ISBN: (纸本)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. © 2010 IEEE.

关键词： XML

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Updating Approximations of VPRS Model based on Dominance Relations

Updating Approximations of VPRS Model based on Dominance Rel...

引用

作者： Jiajia HOU Yan LI Yan ZHAN Liang WANG Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei University

In real world problems, the collected data vary from time to time, and therefore, the approximations of a concept by a variable precision rough set model(VPRS) should be correspondingly updated. This paper focuses on developing incremental method to update set approximations of VPRS based on dominance relations. Under dynamic environments where an object is inserted or deleted, we present the updating principles and then develop the incremental method for updating approximation sets. The related theoretical results are presented with proofs, and illustrative examples are also given to support the effectiveness of the proposed method.

关键词： Variable Precision Rough Set Dominance Relation Set Approximation Dynamic Information Systems Incremental Update

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