We study the applicability of partially supervised text classification to junk mail filtering, where a given set of junk messages serve as positive examples while the messages received by a user are unlabeled examples, but there are no negative examples. Supplying a junk mail filter with a large set of junk mails could result in an algorithm that learns to filter junk mail without user intervention and thus would significantly improve the usability of an e-mail client. We study several learning algorithms that take care of the unlabeled examples in different ways and present experimental results. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Schneider, K. M. (2005). Learning to filter junk E-mail from positive and unlabeled examples. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3248, pp. 426–435). Springer Verlag. https://doi.org/10.1007/978-3-540-30211-7_45
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