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Unformatted text preview: m Filtering of Emails The explosive growth of unsolicited e-mail, more commonly known as spam, over the last years has been undermining constantly the usability of e-mail. One solution is offered by anti-spam filters. Most commercially available filters use black-lists and hand-crafted rules. On the other hand, the success of machine learning methods in text classification offers the possibility to arrive at anti-spam filters that quickly may be adapted to new types of spam. There is a growing number of learning spam filters mostly using naive Bayes classifiers. A prominent example is Mozilla’s e-mail client. Michelakis et al. (2004) compare different classifier methods and investigate different costs of classifying a proper mail as spam. They find that for their benchmark corpora the SVM nearly always yields best results. To explore how well a learning-based filter performs in real life, they used an SVM-based procedure for seven months without retraining. They achieved a prec...
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This note was uploaded on 06/19/2011 for the course IT 2258 taught by Professor Aymenali during the Summer '11 term at Abu Dhabi University.

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