The study on Gauss kernel function in Support Vector Machine

The kernel function is the crucial ingredient of SVM. In a great of kernel functions, many researchers attach importance to Gauss kernel function because of its peculiar property and broad application. This paper mainly discusses the selection methods of Gauss kernel radius and punish-parameter C and suggests the rule of maximizing the ratio of within-class distance to between-class distance. Through emulator, we compare it with the known method and find this technique is effective.