Minimal learning machine: a new distance-based method for supervised learning

Abstract

In this work, a novel supervised learning method, the Minimal Learning Machine (MLM), is proposed. Learning a MLM consists in reconstructing the mapping existing between input and output distance matrices and then estimating the response from the geometrical configuration of the output points. Given its general formulation, the Minimal Learning Machine is inherently capable to operate on nonlinear regression problems as well as on multidimensional response spaces. In addition, an intuitive extension of the MLM is proposed to deal with classification problems. On the basis of our experiments, the Minimal Learning Machine is able to achieve accuracies that are comparable to many de facto standard methods for regression and it offers a computationally valid alternative to such approaches.