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Machine Learning for Spam Detection
The Hong Kong University of Science and Technology Department of Computer Science and Engineering FYT Presentation and Demonstration Title: "Machine Learning for Spam Detection" by Mr. Yau Wing Pong, Patrick Abstract Many current spam filters make use of machine learning techniques to classify emails as either spam or legitimate mails before filtering out the spam mails. However, some emails, known as gray mails, cannot be classified as spam or legitimate mails easily, partly because different email users have different preferences on these emails. This poses great challenges to the design of spam filters. In this paper, we explore the feasibility of using a two-stage spam filter to tackle gray mails. In the first stage, the spam filter first detects gray mails and then classifies them as either spam or legitimate mails using a na?ve Bayes classifier. In the second stage, all the remaining gray mails are classified using a support vector machine. We have performed extensive experiments to compare the performance of one-stage and two-stage filters with respect to both accuracy and efficiency. Date : 28 April 2008, Monday Time : 11am to 12pm Venue : Room 3304 Advisor : Dr. D.Y. Yeung 2nd Reader : Dr. Brian Mak