重庆邮电大学学报（自然科学版）2011，Vol.23Issue(1)：91-95,5.DOI:10.3979/j.issn.1673-825X.2011.01.019
一种基于AFSA的SVM分类方法
A classification method of SVM based on AFSA
摘要
Abstract
In this paper, artificial fish swarm algorithm (AFSA) that is a global search method to optimize the parameters of support vector machines (SVM ) is applied and modified for image classification. In the classification, firstly, the range of parameters of punishment C and kernel function δ2 are initialized; secondly, AFSA is applied to optimize the parameters to gain suitable values; finally, SVM is used for classification, in which the parameters are optimized. By comparing with C-SVC and cross-validate methods, the result excelled another two methods, so the studied algorithm of AFSA-SVM is more accuracy and robust.关键词
人工鱼群算法(AFSA)/支持向量基(SVM)/C-SVC/交叉验证法Key words
artificial fish swarm algorithm (AFSA)/ support vector machines (SVM)/ C-SVC/ cross-validate分类
信息技术与安全科学引用本文复制引用
王卫星,刘娟..一种基于AFSA的SVM分类方法[J].重庆邮电大学学报（自然科学版）,2011,23(1):91-95,5.基金项目
The National Natural Sciences Foundation of China (60873186) （60873186）
