A GP-based kernel construction and optimization method for RVM

Abstract

Selecting a suitable kernel for relevance vector machine is one of most challenging aspects of successfully using this learning tool. Efficiently automating the search for such a kernel is therefore desirable. This paper proposes a data-driven kernel function construction and optimization method, which combines genetic programming (GP) and relevance vector regression to evolve an optimal or near-optimal kernel function, named GP-Kernel. The evolved kernel is compared to several widely used kernels on several regression benchmark datasets. Empirical results demonstrate that RVM using such GP-Kernel can outperform or match the best performance of standard kernels.