Document preprocessing for Naive Bayes classification and clustering with mixture of multinomials

KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2004, Pages 829-834

  Pavlov, Dmitry ^a   Balasubramanyan, Ramnath ^a   Dom, Byron ^a   Kapur, Shyam ^a   Parikh, Jignashu ^a  

^a YAHOO INC   (United States)

Author keywords

Classification; Clustering; Data transformations; Mixture of multinomials; Naive Bayes; Performance

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; DATA ACQUISITION; HEURISTIC METHODS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; PERFORMANCE; PROBLEM SOLVING;

CLASSIFICATION; CLUSTERING; DATA TRANSFORMATIONS; MIXTURE OF MULTINOMIALS; NAIVE BAYES;

DATA PROCESSING;

EID: 12244308645     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1014052.1016922     Document Type: Conference Paper

References (11)

1. A. Berger. Error-correcting output coding for text classification. In IJCAI Workshop on machine learning for information filtering, 1999.
2. Pedro Domingos and Michael J. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2-3); 103-130, 1997.
3. R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. John Wiley and Sons, 1973.
4. I.Csiszar. A geometric interpretation of darroch and ratcliff's generalized iterative scaling. The Annals of Mathematical Statistics, 17(3):1409-1413, 1989.
5. A. McCallum and K. Nigam. A comparison of event models for naive bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization, 1998.
6. G. McLachlan and K. Basford. Mixture Models. Marcel Dekker, New York, 1988.
7. D. Pavlov, J. Mao, and B. Dom. Scaling-up support vector machines using boosting algorithm. In International Conference on Pattern Recognition (ICPR)., 2000.
8. Dmitry Pavlov, Darya Chudova, and Padhraic Smyth. Towards scalable support vector machines using squashing. In Knowledge Discovery and Data Mining, pages 295-299, 2000.
9. J. Platt. Using sparseness and analytic QP to speed training of support vector machines. In Advances in Neural Information Processing Systems, 1999.
10. J. Rennie, L. Shih, J. Teevan, and D. Karger. Tackling the poor assumptions of naive bayes text classifiers. In ICML, 2003.
11. R. Vilalta and I. Rish. A decomposition of classes via clustering to explain and improve naive bayes. In Proceedings of European Conference on Machine Learning, pages 444-455, 2003.