Abstract: Parameters of support vector machine are important factors to affect the performance of SVM.Parameters selection of SVM based on differential evolution is presented to improve the classification accuracy and generalization ability.Optimal rule is the least misjudgment rate of samples，and the parameters of SVM are optimized by using DE.The simulation results show that parameters selection based on DE can accelerate the pace of the search parameters and improve classification accuracy of SVM.It has good robustness and strong global search capability.

Key words: Support Vector Machine（SVM）, Differential Evolution（DE）, parameters selection

摘要： 支持向量机参数是影响其性能的重要因素，为了进一步提高支持向量机分类精度和泛化能力，提出了基于差分进化算法的SVM参数选择。以样本误判率最小为优化准则，利用差分进化算法对SVM参数进行优化选择。实验结果表明，利用差分进化算法选择SVM参数，加快了参数搜索的速度，提高了SVM分类精度，该方法具有良好的鲁棒性和较强的全局寻优能力。

关键词: 支持向量机, 差分进化算法, 参数选择