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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 self-training and transductive support vector machines yield inferior classifiers to those constructed using supervised learning on thedoi:10.1145/1390334.1390482 dblp:conf/sigir/MojdehC08 fatcat:edmwaqhbtngnrkersrb63vbdf4