Extending the Minimal Learning Machine for Pattern Classification

Abstract

The Minimal Learning Machine (MLM) has been recently proposed as a novel supervised learning method for regression problems aiming at reconstructing the mapping between input and output distance matrices. Estimation of the response is then achieved from the geometrical configuration of the output points. Thanks to its comprehensive formulation, the MLM is inherently capable of dealing with nonlinear problems and multidimensional output spaces. In this paper, we introduce an extension of the MLM to classification tasks, thus providing a unified framework for multiresponse regression and classification problems. On the basis of our experiments, the MLM achieves results that are comparable to many de facto standard methods for classification with the advantage of offering a computationally lighter alternative to such approaches.