Mona Mojdeh

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A graph based semi-supervised method for email spam filtering, based on the local and global consistency method, yields low error rates with very few labeled examples. The motivating application of this method is spam filters with access to very few labeled message. For example, during the initial deployment of a spam filter, only a handful of labeled(More)
1 Overview For the TREC 2009, we exhaustively classified every document in each corpus, using machine learning methods that had previously been shown to work well for email spam [9, 3]. We treated each document as a sequence of bytes, with no tokenization or parsing of tags or meta-information. This approach was used exclusively for the adhoc web, diversity(More)
Using two email streams, we show that a personal filter trained exclusively on user feedback substantially outper-forms (p ≈ 0.000) three industry-leading global spam filters not using feedback. We show that autonomous personal filters , trained on the output from a global spam filter rather than user feedback, substantially outperform (p ≈ 0.000) the(More)
We describe a plugin extension to the Thun-derbird Mail Client to support standardized evaluation of multiple spam filters on private mail streams. Researchers need not view or handle the subject users' messages and subject users need not be familiar with spam filter evaluation methodology. All that is required of the user is to install the plugin as a(More)
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