Parameters Selection for Support Vector Machine Based on Particle Swarm Optimization

被引：0

作者：

Li, Jun ^{[1
]}

Li, Bo ^{[1
]}

机构：

[1] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan, Peoples R China

来源：

INTELLIGENT COMPUTING THEORY | 2014年 / 8588卷

关键词：

high-dimentional classfication; support vector machine(SVM); particle swarm optimization(PSO);

D O I：

暂无

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 paper, an SVM classification system based on particle swarm optimization (PSO) is proposed to improve the generalization performance of the SVM classifier. Authors have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function. The experiments are conducted on the basis of benchmark dataset. Fourteen obtained results clearly confirm the superiority of the PSO-SVM approach.

引用

页码：41 / 47

页数：7

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