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Abstract : |
The rise of the World Wide Web and the ever-increasing amounts of machine-readable text has caused text classication to become a important aspect of machine learning. One speci c application that has the potential to aect almost every user of the Internet is e-mail ltering. 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 lter e-mail have yet to signicantly aect 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 classication techniques to lter personal e-mail. I also present a ltering system, ile, that is both eective and ecient, 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 ile could become a useful and valuable part of any e-mail client., |
