Least Squares Support Vector Machine Classifiers

被引：36

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

页码：293 / 300

页数：7

共 10 条

[1]

Ridella S.(1997)Circular back propagation networks for classification IEEE Transactions on Neural Networks 8 84-97

[2]

Rovetta S.(1997)Comparing support vector machines with Gaussian kernels to radial basis function classifiers IEEE Transactions on Signal Processing 45 2758-2765

[3]

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