Pseudo Gradient and Dynamic Step Optimization Algorithm for RBF-SVM Parameter Search

To the issue of hyper-parameter selection for radial basis function (RBF) based support vector machines (SVM), a new algorithm named as pseudo gradient and dynamic step optimization is proposed. Based on the characteristics of RBF, the kernel parameter is pre-estimated according to the distribution of the train set and the logarithmic scale is employed for the parameter space. The search direction is estimated with the changing of classification accuracy and by tuning the search step accordingly. At last, comparative experiments with Grid approach and PSO algorithm indicate the validity of the proposed algorithm.