The rise of the World Wide Web and the ever-increasing amounts of machine-readable text has caused text classification to become a important aspect of machine learning. One specific application that has the potential to affect almost every user of the Internet is e-mail filtering. The WorldTalk Corporation estimates that over 60 million business people use e-mail [6]. Many more use e-mail purely on a personal basis and the pool of e-mail users is growing daily. And yet, automated techniques for learning to filter e-mail have yet to significantly affect the e-mail market. Here, I attack problems that plague practical e-mail ltering and suggest solutions that will bring us closer to the acceptance of using automated classification techniques to filter personal e-mail. I also present a filtering system, ifile, that is both effective and efficient, and which has been adapted to a popular e-mail client. Results are presented from a number of experiments and show that a system such as ifile could become a...

We propose a novel feature selection method based on a Variable Memory Markov model (VMM). The VMM was originally proposed as a generative model trying to preserve the original source statistics from training data.

We introduce a new algorithm for the induction of classi ers from data, based on Bayesian networks. Basically this problem has already been examined from two perspectives: rst, the induction of classi ers by learning algorithms for Bayesian networks, second, the induction of classi ers based on the naive Bayesian classi er. Our approach is located between these two perspectives; it eliminates the disadvantages of both while exploiting their advantages. In contrast to recently appeared re nements of the naive Bayes classi er, which captures single correlations in the data, we have developed an approach which captures multiple correlations and furthermore does a trade-o between complexity and accuracy. In this paper we evaluate the implementation of our approach with data sets from the machine learning repository and data sets arti cially generated by Bayesian networks.

We propose a novel feature selection method, based on a variable memory Markov model (VMM). The VMM was originally proposed as a generative model for language and handwriting[14], trying to preserve the original source statistics from training data. Here we consider the VMM models for extracting discriminative statistics for hypotheses testing.