Gaussian opposite maps for reduced-set relevance vector machines

Abstract

The Relevance Vector Machine is a bayesian method. This model represents its decision boundary using a subset of points from the training set, called relevance vectors. The training algorithm of that is time consuming. In this paper we propose a technique for initialize the training process using the points of an opposite map in classification problems. This solution approximate the relevance points of the solutions obtained by Support Vector Machines. In order to assess the performance of our proposal, we carried out experiments on well-known datasets against the original RVM and SVM. The GOM-RVM achieved accuracy equivalent or superior than to SVM and RVM with fewer relevance vectors.