An Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machines
Soft-margin support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the normalized kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding soft-margin SVMs can obtain more accurate or at least equal performance than the soft-margin SVMs by applying k-fold cross-validation to determine the parameters.
normalized kernel soft-margin support vector machine svm k-fold cross-validation kernel method optimal kernel