关键词: 支持向量机（SVM）, 模型选择, 多目标优化（MOO）, 粒子群优化（PSO）

Abstract: To meet the different focuses on the performances of Support Vector Machine（SVM） during application，the model selection of SVM is taken as a Multi-Object Optimization（MOO） problem.Particle Swarm Optimization（PSO） is applied to solve this MOO problem.The solution known as Pareto front is gained and one can select a concrete single solution from it according to own application needs，i.e.final model selection.The experiments on several datasets show that the method can gain Pareto front faster and the elements in the Pareto set can meet the need on generalization performance and training speed in SVM application.

Key words: Support Vector Machine（SVM）, model selection, Multi-Object Optimization（MOO）, Particle Swarm Optimization（PSO）