Spam Filtering Using Statistical Data Compression Models

  title={Spam Filtering Using Statistical Data Compression Models},
  author={Andrej Bratko and Gordon V. Cormack and Bogdan Filipic and Thomas R. Lynam and Blaz Zupan},
  journal={Journal of Machine Learning Research},
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task calls for fast, incremental and robust learning algorithms. In this paper, we investigate a novel approach to spam filtering based on adaptive statistical data compression models. The nature of these… CONTINUE READING
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In Proc

  • L. A. Breyer. DBACL at the TREC
  • 14th Text REtrieval Conference (TREC 2005…
  • 2005
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