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Bayesian online classifiers for text classification and filtering
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Tampere, Finland
SESSION: Filtering table of contents
Pages: 97 - 104  
Year of Publication: 2002
ISBN:1-58113-561-0
Authors
Kian Ming Adam Chai  DSO National Laboratories, Singapore
Hai Leong Chieu  DSO National Laboratories, Singapore
Hwee Tou Ng  National University of Singapore, Singapore
Sponsor
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 16,   Downloads (12 Months): 93,   Citation Count: 13
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ABSTRACT

This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of continuous learning in the batch-adaptive text filtering task.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS 2000, volume 13. The MIT Press, 2001.
 
2
D. Cox and E. Snell. Analysis of Binary Data. Chapman & Hall, London, 2nd edition, 1989.
 
3
L. Csató and M. Opper. Sparse representation for Gaussian process models. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS 2000, volume 13. The MIT Press, 2001.
 
4
 
5
 
6
 
7
 
8
 
9
10
11
 
12
 
13
 
14
R. M. Neal. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical Report CRG-TR-97-2, Department of Computer Science, University of Toronto, January 1997.
15
 
16
M. Opper. Online versus offline learning from random examples: General results. Physical Review Letters, 77:4671--4674, 1996.
 
17
 
18
S. Robertson and D. A. Hull. The TREC-9 filtering track final report. In Proceedings of the 9th Text REtrieval Conference (TREC-9), pages 25--40, 2001.
 
19
 
20
 
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N. A. Syed, H. Liu, and K. K. Sung. Incremental learning with support vector machines. In Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Artificial Intelligence (IJCAI-99), 1999.
 
22
 
23
 
24
C. K. Williams and M. Seeger. Using the Nyström method to speed up kernel machines. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS 2000, volume 13. The MIT Press, 2001.
 
25
O. Winther. Bayesian Mean Field Algorithms for Neural Networks and Gaussian Processes. PhD thesis, University of Copenhagen, CONNECT, The Niels Bohr Institute, 1998.
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CITED BY  13

Collaborative Colleagues:
Kian Ming Adam Chai: colleagues
Hai Leong Chieu: colleagues
Hwee Tou Ng: colleagues