Search Strategies Guided by the Evidence for the Selection of Basis Functions in Regression

Abstract

This work addresses the problem of selecting a subset of basis functions for a model linear in the parameters for regression tasks. Basis functions from a set of candidates are explicitly selected with search methods coming from the feature selection field. Following approximate Bayesian inference, the search is guided by the evidence. The tradeoff between model complexity and computational cost can be controlled by choosing the search strategy. The experimental results show that, under mild assumptions, compact and very competitive models are usually found.