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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 and(More)
The results of the 2006 ECML/PKDD Discovery Challenge suggest that semi-supervised learning methods work well for spam filtering when the source of available labeled examples differs from those to be classified. We have attempted to reproduce these results using data from the 2005 and 2007 TREC Spam Track, and have found the opposite effect: methods like(More)
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)
Formal verification techniques are used to obtain correct and reliable systems. In this paper we use the actor-based language, Rebeca, for modeling the CSMA/CD Protocol. In Rebeca, each component in the system is modeled as a reactive object. Reactive objects are encapsulated, with no shared variables, communicating via asynchronous message passing. Rebeca(More)
The need for broadband wireless access systems in residential and small-to-medium sized business environments is increasing due to their requirement for higher bandwidth network access. IEEE 802.16 Air interface standard is truly a state-of-art specification for fixed broadband wireless access systems employing a point-to-multipoint (PMP) architecture.(More)
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