Spam filtering using a markov random field model with variable weighting schemas

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 347-350

  Chhabra, Shalendra ^a   Yerazunis, William S ^b   Siefkes, Christian ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

^c FREIE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FRAMEWORK; FEATURE VECTORS; MARKOV RANDOM FIELD (MRF); SPAM CLASSIFICATION;

ELECTRONIC MAIL; FEATURE EXTRACTION; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY; PROBLEM SOLVING; SET THEORY; VECTORS;

SPAMMING;

EID: 19544362523     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2004.10031     Document Type: Conference Paper

References (10)

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3. CRM114 Discriminator - The Controllable Regex Mutilator. http://crm114.sourceforge.net/.
4. J. M. Hammersley and P. Clifford. Markov Field on Finite Graphs and Lattices. In Unpublished, 1971.
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7. C. Siefkes, F. Assis, S. Chhabra, and W. S. Yerazunis. Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering. In Proceedings of ECML/PKDD 2004, LNCS. Springer Verlag, 2004.
8. TIES - Trainable Incremental Extraction System. http://www.inf.fu-berlin.de/inst/ag-db/software/ties/.
9. W. S. Yerazunis. Sparse Binary Polynomial Hashing and the CRM114 Discriminator. In MIT Spam Conference, 2003.
10. W. S. Yerazunis. The Spam Filtering Accuracy Plateau at 99.9 Percent Accuracy and How to Get Past It. In MIT Spam Conference, 2004.