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A generalized S-K algorithm for learning v-SVM classifiers
Tao, Q; Wu, GW; Wang, J
2004-07-16
发表期刊PATTERN RECOGNITION LETTERS
ISSN0167-8655
卷号25期号:10页码:1165-1171
摘要The S-K algorithm (Schlesinger-Kozinec algorithm) and the modified kernel technique due to Friess et al. have been recently combined to solve SVM with L-2 cost function. In this paper, we generalize S-K algorithm to be applied for soft convex hulls. As a result, our algorithm can solve v-SVM based on L-1 cost function. Simple in nature, our soft algorithm is essentially a algorithm for finding the epsilon-optimal nearest points between two soft convex hulls. As only the vertexes of the hard convex hulls are used, the obvious superiority of our algorithm is that it has almost the same computational cost as that of the hard S-K algorithm. The theoretical analysis and some experiments demonstrate the performance of our algorithm. (C) 2004 Elsevier B.V. All rights reserved.
关键词statistical machine learning support vector machines classification v-SVM S-K algorithms soft convex hulls
DOI10.1016/j.patrec.2004.03.011
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000222392000008
出版者ELSEVIER SCIENCE BV
引用统计
被引频次:19[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/9901
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, Q
作者单位1.New Star Res Inst Appl Tech, Hefei 230031, Peoples R China
2.Chinese Acad Sci, Comp Technol Inst, Bioinformat Lab, Beijing 100080, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
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Tao, Q,Wu, GW,Wang, J. A generalized S-K algorithm for learning v-SVM classifiers[J]. PATTERN RECOGNITION LETTERS,2004,25(10):1165-1171.
APA Tao, Q,Wu, GW,&Wang, J.(2004).A generalized S-K algorithm for learning v-SVM classifiers.PATTERN RECOGNITION LETTERS,25(10),1165-1171.
MLA Tao, Q,et al."A generalized S-K algorithm for learning v-SVM classifiers".PATTERN RECOGNITION LETTERS 25.10(2004):1165-1171.
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