Constrained domain maximum likelihood estimation for naive Bayes text classification

Pattern Analysis and Applications

Volumn 13, Issue 2, 2010, Pages 189-196

  Andrés Ferrer, Jesús ^a   Juan, Alfons ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Karush Kuhn Tucker conditions; Maximum likelihood estimation; Naive Bayes; Parameter smoothing; Text classification

Indexed keywords

EID: 77951765063     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-009-0149-y     Document Type: Article

References (11)

1. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, London.
2. Hoare Z (2007) Landscapes of naïve bayes classifiers. Pattern Anal Appl 11(1): 59-72.
3. Juan A, Ney H (2002) Reversing and smoothing the multinomial naive Bayes text classifier. In Proceedings of PRIS 2002, pp 200-212.
4. Juan A, Vilar D, Ney H (2007) Bridging the gap between Naive Bayes and maximum entropy. In Proceedings of the PRIS 2007, Funchal (Portugal), pp 59-65.
5. Lewis DD (1998) Naive Bayes at forty: the independence assumption in information retrieval. In: Proceedings of the ECML'98, pp 4-15.
6. McCallum A (1998) Bow: a toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.umass.edu/~mccallum/bow/rainbow.
7. McCallum A (2002) Industry sector data set. http://www. cs. umass. edu/~mccallum/code-data. html.
8. McCallum A, Nigam K (1998) A comparison of event models for Naive Bayes text classification. In: Proceedings of AAAI/ICML-98 workshop on learning for text categorization, pp 41-48.
9. Rennie J (2001) Original 20 newsgroups data set, people. csail.mit.edu/jrennie/20Newsgroups.
10. Vidal E et al (2000) Final report of ESPRIT Research Project 30268 (EuTrans).
11. Vilar D, Ney H, Juan A, Vidal E (2004) Effect of feature smoothing methods in text classification tasks. In: Proceedings of PRIS 2004, pp 108-117.