The property of the Kullback-Leibler Kernel for Normalized Frequency Spectrum

Abstract

The kernel classifier that realizes a nonlinear classification such as Support Vector Machine has been successfully implemented in a number of fields. In the kernel method, the appropriate selection or design of the kernel function is important for the construction of a classifier that has high performance. The present paper describes a normalized frequency spectrum classification method using the SVM with the Kullback-Leibler (KL) kernel. We introduce the KL kernel to normalized spectrum classification and study the property of similarity calculation of the KL kernel and other common kernels with respect to the change in the appearance position of spectrum peaks.