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,...
