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A generalized S-K algorithm for learning v-SVM classifiers
Tao, Q; Wu, GW; Wang, J
AbstractThe 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.
KeywordStatistical Machine Learning Support Vector Machines Classification V-svm S-k Algorithms Soft Convex Hulls
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000222392000008
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Cited Times:18[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
Recommended Citation
GB/T 7714
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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