An improved conjugate gradient scheme to the solution of least squares SVM

被引:73
作者
Chu, W
Ong, CJ
Keerthi, SS
机构
[1] UCL, Gatsby Computat Neurosci Unit, London WC1E 6BT, England
[2] Natl Univ Singapore, Dept Engn Mech, Singapore 119260, Singapore
[3] Yahoo, Res Labs, Pasadena, CA 91105 USA
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 02期
关键词
conjugate gradient (CG); least square support vector machines (LS-SVM); sequential minimal optimization (SMO);
D O I
10.1109/TNN.2004.841785
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.
引用
收藏
页码:498 / 501
页数:4
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