Choosing the Kernel parameters of Support Vector Machines According to the Inter-cluster Distance

This paper proposes using the inter-cluster distance between class means in the feature space to help choose parameters for a kernel function when training a support vector machine (SVM). With the proposed method, the square values of the distance between the two class means of the training data in different feature spaces are calculated. These values are used as the indexes of data separation in the feature space. The experiment results show that the proposed method can choose the parameters close to the best ones. As a result, fewer possible values of the kernel parameters are required to be tested when training an SVM, and thus the training time of total training process can be significantly shortened.