摘要
SVM是一种新型的机器学习方法,其分类性能的优劣主要受核函数及核参数的影响,国内外学者针对SVM核参数的选择已提出许多算法.本文首先分析了RBF核参数对SVM分类性能的影响,然后又对比分析了目前存在的几种基于RBF核的SVM核参数选择方法.通过实验,发现使用遗传算法选择核参数的SVM有比较快的搜索速度.
Support vector machine（SVM） is a new kind of machine learning methods, whose classification performance is affected by kernel function and kernel parameters. Nowadays some researchers have proposed many algorithms about choosing kernel parameters. This paper emphasizes on analyzing kernel parameters choice algorithms of SVM with RBF kernel. The paper finds that genetic algorithm which is used in choosing kernel parameters could improve search velocity of SVM.
出处
《新疆大学学报（自然科学版）》
CAS
2009年第3期355-358,363,共5页
Journal of Xinjiang University(Natural Science Edition)
关键词
支持向量机
RBF核
遗传算法
support vector machine
rbf kernel
genetic algorithm
