Least Squares Support Vector Machine Classifiers

被引：39
|
作者：
J.A.K. Suykens
J. Vandewalle
机构：
[1] Katholieke Universiteit Leuven,Department of Electrical Engineering
来源：
Neural Processing Letters | 1999年 / 9卷
关键词：
classification; support vector machines; linear least squares; radial basis function kernel;
D O I：
暂无
中图分类号：
学科分类号：
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
收藏
页码：293 / 300
页数：7
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