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A general soft method for learning SVM classifiers with L-1-norm penalty
Tao, Qing; Wu, Gao-Wei; Wang, Jue
2008-03-01
发表期刊PATTERN RECOGNITION
卷号41期号:3页码:939-948
文章类型Article
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.
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
WOS标题词Science & Technology ; Technology
关键词[WOS]ITERATIVE ALGORITHM ; SUPPORT ; POINT
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000251357100015
引用统计
被引频次:25[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9619
专题09年以前成果
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
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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.
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