A model selection criterion for classification: application to HMM topology optimization

Abstract

This paper proposes a model selection criterion for classification problems. The criterion focuses on selecting models that are discriminant instead of models based on the Occam's razor principle of parsimony between accurate modeling and complexity. The criterion, dubbed discriminative information criterion (DIC), is applied to the optimization of hidden Markov model topology aimed at the recognition of cursively-handwritten digits. The results show that DIC-generated models achieve 18% relative improvement in performance from a baseline system generated by the Bayesian information criterion (BIC).