A TUTORIAL ON RELEVANCE VECTOR MACHINES FOR REGRESSION AND CLASSIFICATION WITH APPLICATIONS

Relevance vector machines (RVM) have recently attracted much interest in the research community because they provide a number of advantages. They are based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. As a consequence, they can generalize well and provide inferences at low computational cost. In this tutorial we first present the basic theory of RVM for regression and classification, followed by two examples illustrating the application of RVM for object detection and classification. The first example is target detection in images and RVM is used in a regression context. The second example is detection and classification of microcalcifications from mammograms and RVM is used in a classification framework. Both examples illustrate the application of the RVM methodology and demonstrate its advantages. 1