Least squares support vector machine classifiers

被引：9311

作者：

Suykens, JAK ^{[1
]}

Vandewalle, J ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Elect Engn, ESAT, SISTA, B-3001 Heverlee, Belgium

来源：

NEURAL PROCESSING LETTERS | 1999年 / 9卷 / 03期

关键词：

classification; support vector machines; linear least squares; radial basis function kernel;

D O I：

10.1023/A:1018628609742

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.

引用

页码：293 / 300

页数：8

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