Max-Min Method for Kernel-Parameter Selection in SVM

A distance is defined between two sample points in high-dimension space by means of kernel-function technique.A distance-square matrix of different classes is called.A new method,max-min,using sample distance between different classes,without employing standard samples to train to find optimal or effective kernel parameter,is proposed for kernel-parameter selection in support vector machines.The method avoid the deficiencies of a large number of calculation of conventional methods which depend on experience strongly.Radial basis kernel function and polynomial kernel function are used as examples respectively to conduct experiments to show steps using the algorithm.By combining the experiment results,a conclusion is concluded that a problem of choosing kernel parameter,a problem of multi-objective optimization,does not exist optimal value but effective value in an open set.The available experiment results is cited to support sufficiently above conclusion.The max-min method not only provides theoretically a method of optimization of kernel-parameter selection but also has guide operation for the parameter choice by means of experiments.