关键词: 支持向量机（SVM）, 核参数选择, 特征空间, 可分性度量

Abstract: Support Vector Machine（SVM） is a kernel-based method，kernel function and kernel parameter selection directly affect SVM model’s generalization ability.A popular kernel parameter selection method is the grid search method.Large computation quantity of this method makes the training process time-consuming.This paper proposes the new method which using the Separability Measure（SM） between classes in the feature space to choose the kernel parameter.Calculating such SM costs much less computation time than training the corresponding SVM models，thus the best kernel parameter can be chosen much faster，and the testing accuracy of trained SVM by the proposed method is competitive to the standard ones.Experiment results show that the proposed method is feasible and effective.

Key words: Support Vector Machine（SVM）, kernel parameter selection, feature space, Separability Measure（SM）