Simple algorithms for least square support vector machines

被引：3
|
作者：
Hsu-Kun Wu [1 ]
Pao-Jung Chen [1 ]
Jer-Guang Hsieh [1 ]
机构：
[1] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 804, Taiwan
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS | 2006年
关键词：
D O I：
10.1109/ICSMC.2006.385118
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose five simple algorithms for solving the least square support vector machine (LS-SVM) learning problems. For linear regression, we first present a Widrow-Hoff-like algorithm for the primal optimization problem. The dual form of this algorithm is then provided. For kernel-based nonlinear LS-SVM, we first present a Widrow-Hoff-like algorithm. The elegant and powerful two-parameter sequential minimization optimization (2P-SMO) algorithm is then provided. Finally, we give a detailed derivation of the three-parameter sequential minimization optimization (3P-SMO) algorithm. A numerical example is provided.
引用
收藏
页码：5106 / +
页数：2
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