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 |
| 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标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | ITERATIVE ALGORITHM ; SUPPORT ; POINT |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000251357100015 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9619]  |
| 专题 | 自动化研究所_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 |
| 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：自动化研究所
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