Online Linear Discriminative Learning for Spam Filter

Abstract

This paper describes a simple but effective discriminative learner for spam filter. We statically derive the features within Bayesian framework, cluster them into groups according to their position and then assigning weights respectively. The model is evaluated by TREC Spam corpus and compared with the TREC results. Experimental results show that our linear discriminative model can produce competitive results.