Efficient model selection for Support Vector Machine with Gaussian kernel function

Support vector machine(SVM) has become a powerful and widely used machine learning method in resent years. Gaussian kernel is the most commonly used kernel function. However, model selection including setting the width parameter sigma in kernel function and the regularization parameter C is essential to generalization performance of SVM. In this paper we proposed a new parameter selection method for Support Vector Machine. The key idea of our method MSKD in selecting the Gaussian kernel parameter is that convergent character between pattern´s similarity measurement in feature space will decrease the classification ability of SVM. In addition, We combined MSKD algorithm with one-dimension search strategy based on cross-validation and developed a complex parameters selection method named MSKD-GS. Experiments on eight real world data sets from UCI have been carried out to demonstrate the effectiveness and efficiency of this method.