关键词: 支持向量机, 属性权重, 推广高斯核, 交叉验证, 粒子群优化

Abstract: In support vector classification，different features effect differently on classification.For this problem，a generalized Gaussian kernel function is proposed by using features weight，particle swarm optimization is used to do the model selection with the k-fold cross-validation error as the fitness.Then experiment results on UCI dataset show that the method can improve the performance of SVM.

Key words: support vector machine, feature weight, generalized Gaussian kernel, cross validation, particle swarm optimization