Optimizing parameters of support vector machine based on gradient algorithm

被引：0
|
作者：
School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu 610054, China [1 ]
机构：
来源：
Kongzhi yu Juece/Control and Decision | 2008年 / 23卷 / 11期
关键词：
Parameter estimation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Some recent methods of parameters' optimization are analyzed, and several characteristics of efficient arithmetics to optimize parameters of support vector machine are generalized. Gradient algorithm can not be utilized directly because of its requirement of differentiable function. In the new method proposed, gradient direction is not directional derivative but optimized result of chaos search in local area. This method doesn't require differentiable function, and has the advantage of faster convergent speed, the ability of optimization within global scope and the independence between eventual optimized paramaters and initial SVM paramaters.
引用
收藏
页码：1291 / 1295
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