A Relevance Vector Machine Prediction Method Based on the Biased Wavelet Kernel Function

Abstract

Relevance Vector Machine (RVM) is an important learning method in the field of machine learning for its sparsity, global optimality and the ability to solve nonlinear problems by using kernel functions. In this paper, a family of biased wavelets was used to construct the kernel functions of RVM. Biased wavelet have adjustable nonzero mean which makes the kernel of RVM more flexible. With the kernel method of the Centered Kernel Target Alignment (CKTA), the biased parameter was selected to improve the prediction performance of RVM model. The algorithm based on the biased wavelet kernel showed an increased prediction accuracy compared to using wavelet kernel and Cauchy kernel. In short, Relevance Vector Machine with the biased wavelet kernel is a flexible prediction algorithm with high prediction accuracy.