Maximum Entropy with Maximum J-Divergence Discrimination for Text Classification

In this paper we propose a maximum entropy classification method with feature selection. Unlike many maximum entropy methods we follow generative approach, but enforce maximum discrimination with respect to feature selection. By employing conditional independence we present a linear time algorithm for classification and feature selection. Performance and comparitive study of the proposed algorithm has been demonstrated on some large datasets that show our method scale up very well with large datasets.