An Intelligent Model Selection Scheme Based on Particle Swarm Optimization

摘要：

To improve the learning efficiency of support vector machine, an intelligent model selection scheme based on particle swarm optimization (PSO) was presented to optimize the hyper-parameters. By taking the model selection problem as a multi-object optimization problem, one can obtain a solution set known as Pareto front; each one model in this set is nondominated. PSO was used to solve the above mutiobjective optimization problem and then the model set was obtained. The scheme was tested on several datasets, the results show that Pareto front can be obtained in one trial and the effect of every single parameter can be displayed more directly.

关键词： intelligent model selection particle swarm optimization(PSO) multi-object optimization Pareto front support vector machine

作者: Jingtao Huang Xiaomei Chi Jianwei Ma

作者单位: Electronic and Information Engineering College Henan University of Science and Technology Luoyang,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 882-886

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）