Automatic model selection via corrected error backpropagation

Abstract

In this paper, we propose a learning method called Corrected Error Backpropagation which maximizes the corrected log-likelihood which works like Akaike Information Criterion. For the purpose of maximizing the corrected log-likelihood, we introduce temperature parameter for the corrected log-likelihood. This paper also shows an optimal scheduling of the temperature parameter. Applying to our method to a linear regression model on the Boston house price estimation problem and multi layered perceptrons on the DELVE datasets, the method gives good results. © 2009 Springer Berlin Heidelberg.