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A general soft method for learning SVM classifiers with L-1-norm penalty

文献类型：期刊论文

作者Tao, Qing; Wu, Gao-Wei; Wang, Jue
刊名PATTERN RECOGNITION
出版日期2008-03-01
卷号41期号:3页码:939-948
关键词support vector machines classification nu-SVMs nearest points Gilbert's algorithms Schlesinger-Kozinec's algorithms Mitchell-Dem'yanov-Malozemov's algorithms soft convex hulls
ISSN号0031-3203
DOI10.1016/j.patcog.2007.08.004
英文摘要Based on the geometric interpretation of support vector machines (SVMs), this paper presents a general technique that allows almost all the existing L-2-norm penalty based geometric algorithms, including Gilbert's algorithm, Schlesinger-Kozinec's (SK) algorithm and Mitchell-Dem'yanov-Malozemov's (MDM) algorithm, to be softened to achieve the corresponding learning L-1-SVM classifiers. Intrinsically, the resulting soft algorithms are to find E-optimal nearest points between two soft convex hulls. Theoretical analysis has indicated that our proposed soft algorithms are essentially generalizations of the corresponding existing hard algorithms, and consequently, they have the same properties of convergence and almost the identical cost of computation. As a specific example, the problem of solving nu-SVMs by the proposed soft MDM algorithm is investigated and the corresponding solution procedure is specified and analyzed. To validate the general soft technique, several real classification experiments are conducted with the proposed L-1-norm based MDM algorithms and numerical results have demonstrated that their performance is competitive to that of the corresponding L-2-norm based algorithms, such as SK and MDM algorithms. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000251357100015
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/11242]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tao, Qing
作者单位1.Chinese Acad Sci, Inst Automat, Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China
2.New Star Res Inst Appl Tech, Hefei 230031, Peoples R China
3.Chinese Acad Sci, Comp Technol Inst, Div Intelligent Software Syst, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 Tao, Qing,Wu, Gao-Wei,Wang, Jue. A general soft method for learning SVM classifiers with L-1-norm penalty[J]. PATTERN RECOGNITION,2008,41(3):939-948.
APA Tao, Qing,Wu, Gao-Wei,&Wang, Jue.(2008).A general soft method for learning SVM classifiers with L-1-norm penalty.PATTERN RECOGNITION,41(3),939-948.
MLA Tao, Qing,et al."A general soft method for learning SVM classifiers with L-1-norm penalty".PATTERN RECOGNITION 41.3(2008):939-948.

入库方式： OAI收割

来源：计算技术研究所

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