咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >Parallel randomized sampling f... 收藏

Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR)

作     者:Lu, Yumao Roychowdhury, Vwani 

作者机构:Yahoo Inc Sunnyvale CA 94089 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA 

出 版 物:《KNOWLEDGE AND INFORMATION SYSTEMS》 

年 卷 期:2008年第14卷第2期

页      面:233-247页

核心收录:

学科分类:0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0701[理学-数学] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

主  题:randomized sampling support vector machine support vector regression parallel algorithm 

摘      要:A parallel randomized support vector machine (PRSVM) and a parallel randomized support vector regression (PRSVR) algorithm based on a randomized sampling technique are proposed in this paper. The proposed PRSVM and PRSVR have four major advantages over previous methods. (1) We prove that the proposed algorithms achieve an average convergence rate that is so far the fastest bounded convergence rate, among all SVM decomposition training algorithms to the best of our knowledge. The fast average convergence bound is achieved by a unique priority based sampling mechanism. (2) Unlike previous work (Provably fast training algorithm for support vector machines, 2001) the proposed algorithms work for general linear-nonseparable SVM and general non-linear SVR problems. This improvement is achieved by modeling new LP-type problems based on Karush-Kuhn-Tucker optimality conditions. (3) The proposed algorithms are the first parallel version of randomized sampling algorithms for SVM and SVR. Both the analytical convergence bound and the numerical results in a real application show that the proposed algorithm has good scalability. (4) We present demonstrations of the algorithms based on both synthetic data and data obtained from a real word application. Performance comparisons with SVMlight show that the proposed algorithms may be efficiently implemented.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分