Using the Selected Candidate Vectors to Determine Kernel Parameters

This paper proposes an improved scheme of using the inter-cluster distance in the feature space to choose the kernel parameters. First, the candidate vectors of the training set are selected. Then calculate the inter-cluster distance between classes to choose the proper kernel parameters. Finally the selected kernel parameters are used to train the Support Vector Machine (SVM) models. The basic principle is that the Support Vector (SV) set contains all information necessary to solve a given classification task. Experiment results show that our scheme costs much less computation time. Moreover, suitable kernel parameters can also be selected at the same time.