Parameters Selection of Support Vector Machine Using an Improved PSO Algorithm

摘要：

This paper is mainly about the application of Particle Swarm Optimization (PSO) algorithm to specify parameters of Support Vector Machine(SVM).In this paper, sparseness of SVMs solution was introduced to improve fitness function of PSO algorithm.Summation of empirical risk and count of support vectors divided by training sets size was employed as fitness function.Simulation results proved that the improved PSO-SVM algorithm avoided over-fitting problem in parameter optimization process, and prediction precision of SVM was guaranteed.

关键词： Support Vector Machine(SVM) Particle Swarm Optimization(PSO) parameter optimization

作者: PAN Lei LUO Yi

作者单位: Dep.Of Cybernatic Theory and Technology North China Electric Power University BeiJing,China

会议类型: 国际会议

会议名称: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics(第二届智能人机系统与控制论国际学术会议 IHMSC 2010)

会议地点: 南京

会议语种:英文

页码: 535-538

在线出版日期: 2010-08-26（万方平台首次上网日期，不代表论文的发表时间）