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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
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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.
- Summer '11