A Novel Algorithm for Kernel Optimization of Support Vector Machine

摘要：

　　Model optimization namely the kernel function and parameter selection is an important factor to affect the generalization ability of support vector machine (SVM).To solve model optimization problem of support vector machine classifier, a novel algorithm (GC-ABC) is proposed which integrate artificial bee colony algorithm, greedy algorithm and chaos search strategy.The simulation results show that the accuracy of SVM optimized by GC-ABC is superior to the SVM optimized by genetic algorithm and ant colony algorithm.The experiments further suggest that GC-ABC algorithm has fast convergence and strong global search ability, which improves the performance of the support vector machine.

关键词： Kernel Optimization Support Vector Machine Artificial Bee Colony Chaos Search

作者: Lijie Li

作者单位: NingBo City College of Vocational Technology, Xuefu Road, Yinzhou Higher Educational Zone, NingBo, ZheJiang, P.R.China 315100

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 98-105

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）