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The Generalization Ability of SVM Classification Based on Markov Sampling
Jie Xu1; Yuan Yan Tang,4; Bin Zou2; Zongben Xu3; Luoqing Li2; Yang Lu4; Baochang Zhang5,6
2015-06-01
Source PublicationIEEE Transactions on Cybernetics
ISSN21682267
Volume45Issue:6Pages:1169-1179
Abstract

The previously known works studying the generalization ability of support vector machine classification (SVMC) algorithm are usually based on the assumption of independent and identically distributed samples. In this paper, we go far beyond this classical framework by studying the generalization ability of SVMC based on uniformly ergodic Markov chain (u.e.M.c.) samples. We analyze the excess misclassification error of SVMC based on u.e.M.c. samples, and obtain the optimal learning rate of SVMC for u.e.M.c. samples. We also introduce a new Markov sampling algorithm for SVMC to generate u.e.M.c. samples from given dataset, and present the numerical studies on the learning performance of SVMC based on Markov sampling for benchmark datasets. The numerical studies show that the SVMC based on Markov sampling not only has better generalization ability as the number of training samples are bigger, but also the classifiers based on Markov sampling are sparsity when the size of dataset is bigger with regard to the input dimension.

KeywordGeneralization Ability Learning Rate Markov Sampling Support Vector Machine Classification (Svmc)
DOIhttps://doi.org/10.1109/TCYB.2014.2346536
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000354532000006
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
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被引频次[WOS]:16   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorJie Xu; Yuan Yan Tang,; Bin Zou; Zongben Xu; Luoqing Li; Yang Lu; Baochang Zhang
Affiliation1.Faculty of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China
2.Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China
3.Institute for Information and System Science, Xi’an Jiaotong University, Xi’an 710049, China
4.Faculty of Science and Technology, University of Macau 999078, China
5.School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
6.Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, 16163, Genova, Italy
Corresponding Author AffilicationFaculty of Science and Technology
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GB/T 7714
Jie Xu,Yuan Yan Tang,,Bin Zou,et al. The Generalization Ability of SVM Classification Based on Markov Sampling[J]. IEEE Transactions on Cybernetics,2015,45(6):1169-1179.
APA Jie Xu.,Yuan Yan Tang,.,Bin Zou.,Zongben Xu.,Luoqing Li.,...&Baochang Zhang.(2015).The Generalization Ability of SVM Classification Based on Markov Sampling.IEEE Transactions on Cybernetics,45(6),1169-1179.
MLA Jie Xu,et al."The Generalization Ability of SVM Classification Based on Markov Sampling".IEEE Transactions on Cybernetics 45.6(2015):1169-1179.
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