中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
A generalized S-K algorithm for learning v-SVM classifiers

文献类型：期刊论文

作者Tao, Q; Wu, GW; Wang, J
刊名PATTERN RECOGNITION LETTERS
出版日期2004-07-16
卷号25期号:10页码:1165-1171
关键词statistical machine learning support vector machines classification v-SVM S-K algorithms soft convex hulls
ISSN号0167-8655
DOI10.1016/j.patrec.2004.03.011
英文摘要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.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000222392000008
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/9900]  
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.Chinese Acad Sci, Comp Technol Inst, Bioinformat Lab, Beijing 100080, Peoples R China
2.New Star Res Inst Appl Tech, Hefei 230031, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。