Interpolation Based Kernel Function's Construction

The kernel function is important for support vector machines(SVMs) in classifying and regression. However, how to select a kernel function for the given data is still an open problem. Many papers are limited to consult the properties of some standard kernel functions. Since the effect of kernel mapping has not been understood very clearly, the result may be not as good as SVM should be in some cases. In this paper, we first point out that it is no necessary to get the explicit expression of mercer kernel functions, because only some limited values are needed in SVM. Then we present a method based on scattered data interpolation to construct a kernel function according to the given data. The experiments show that kernel function constructed in our method has less subjectivity and more predominance of generalization than most of traditional kernel functions.