Comparison between Minimum Classification Error training and Relevance Vector Machine

Abstract

Discriminative training aims at constructing a classifier that is of small scale but has high classification power. One type, Minimum Classification Error (MCE) training, has been used widely in pattern recognition, especially in the speech recognition field. In parallel with this, Relevance Vector Machine (RVM) has attracted many researchers' interest, based on its potential for alleviating the scalability problem of Support Vector Machine (SVM). It has been reported that RVM achieves high classification accuracy with a limited amount of classifier parameters, i.e., relevance vectors. Comparison studies between MCE training and SVM have been done, but not so much between MCE training and RVM. Motivated by this, we conduct theoretical and experimental comparisons of MCE training and RVM. Results show that MCE training is better suited to the development of small-scale but highly discriminative classifiers than its counterpart.