Sparse representation for kernel function selection of support vector machine

Abstract

Support vector machine (SVM) is a kind of learning method based on the kernel, and it is well known that different kernel functions have significant different influences on the performance of SVM. Thus, how to obtain an effective method for the kernel functions election becomes an important issue in the researching field of SVM. Since different kernel functions contain different geometric measurement characteristics, selecting an appropriate kernel function can results in satisfying generalisation ability of SVM. However, the traditional method for the SVM kernel function choice is manually designated, which contains great limitations and blindness, obviously. Based on sparse representation theory, this paper presents a kernel function selection method, which can take the measurement features of different kernel functions into account and make a reasonable choice of the kernel function of SVM according to specific problems. Finally, simulation experiments are given to show that the method in this paper can overcome the shortcomings of the traditional kernel function selection in SVM model, and achieve the optimal performance.