SMO algorithm for least-squares SVM formulations

被引：99

作者：

Keerthi, SS ^{[1
]}

Shevade, SK

机构：

[1] Natl Univ Singapore, Dept Mech Engn, Singapore 117576, Singapore

[2] Natl Univ Singapore, Genome Inst Singapore, Singapore 117528, Singapore

来源：

NEURAL COMPUTATION | 2003年 / 15卷 / 02期

关键词：

D O I：

10.1162/089976603762553013

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.

引用

页码：487 / 507

页数：21

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