Least squares support vector machine classifiers

被引：9477

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

共 12 条

[1]

Bishop C. M., 1995, NEURAL NETWORKS PATT

[2]

Cherkassky V.S., 1998, LEARNING DATA CONCEP, V1st ed.

[3]

Fletcher R., 1981, PRACTICAL METHODS OP

[4]

Golub GH, 1989, MATRIX COMPUTATIONS

[5]

Haykin S, 1998, NEURAL NETWORKS COMP

[6] Circular backpropagation networks for classification [J].

Ridella, S ;

Rovetta, S ;

Zunino, R .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1997, 8 (01) :84-97

[7]

Saunders C, 1998, Ridge regression learning algorithm in dual variables

[8] Comparing support vector machines with Gaussian kernels to radial basis function classifiers [J].

Scholkopf, B ;

Sung, KK ;

Burges, CJC ;

Girosi, F ;

Niyogi, P ;

Poggio, T ;

Vapnik, V .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1997, 45 (11) :2758-2765

[9]

Vapnik V, 1998, NONLINEAR MODELING, P55

[10]

Vapnik V, 1999, NATURE STAT LEARNING

← 1 2 →