Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning

Proceedings, Twentieth International Conference on Machine Learning

Volumn 2, Issue , 2003, Pages 896-903

  Zhang, Yi ^a   Xu, Wei ^b   Callan, Jamie ^a  

^a Carnegie Mellon University   (United States)

^b NEC LABORATORIES AMERICA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; FEEDBACK; FUNCTIONS; INFORMATION ANALYSIS; LEARNING SYSTEMS; PROBLEM SOLVING; SAMPLING;

ACTIVE LEARNING; BAYESIAN ACTIVE LEARNING; INFORMATION FILTERING; TIME-CRITICAL FILTERING;

ADAPTIVE FILTERING;

EID: 1942484427     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Arampatzis, A., & Hameren, A. (2001). The score-distribution threshold optimization for adaptive binary classification task. Proceedings of the 24th ACM SIGIR Conference.
2. Callan, J. (1996). Document filtering with inference networks. Proc. of the 19th ACM SIGIR Conference.
3. Chai, K. M. A., Chieu, H. L., & Ng, H. T. (2002). Bayesian online classifiers for text classification and filtering. Proceedings of 25th Annual International A CM SIGIR Conference on Research and Development in Information Retrieval. ACM.
4. Freund, Y., Seung, H. S., Shamir, E., & Tishby., N. (1997). Selective sampling using the query by committee algorithm. Machine Learning (pp. 133-168).
5. Hersh, W., Buckley, C., J.Leone, T., & Hickam, D. (1994). OHSUMED: An interactive retrieval evaluation and new large test collection for research. Proceedings of the 7th ACM SIGIR Conference.
6. Joachims, T. (1998). Text categorization with support vector machine. Proceedings of the European Conference on Machine Learning. Springer-Verlag.
7. Lafferty, J., & Zhai, C. (2001). Document language models, query models, and risk minimization for information retrieval. Proceedings of the 24th ACM SIGIR Conference.
8. Lewis, D., & Catlett, J. (1998). Heterogeneous uncertainty sampling for supervised learning. Proceedings of the Eleventh International Conference on Machine Learning. Morgan Kaufmann.
9. McCallum, A., & Nigam, K. (1998). Employing EM in pool-based active learning for text classification. Proceeding of the International Conference on Machine Learing.
10. Robertson, S., & Hull, D. (2001). The TREC-9 Filtering track report. The Ninth Text REtrieval Conference (TREC-9) (pp. 25-40). National Institute of Standards and Technology, special publication 500-249.
11. Robertson, S., & Soboroff, I. (2002). The TREC-10 Filtering track final report. Proceeding of the Tenth Text REtrieval Conference (TREC-10) (pp. 26-37). National Institute of Standards and Technology, special publication 500-250.
12. Robertson, S., & Walker, S. (2000). Threshold setting in adaptive filtering. Journal of Documentation, 312-331.
13. Rocchio, J. J. (1971). Relevance feedback in information retrieval. The SMART Retrieval System- Experiments in Automatic Document Processing (pp. 313-323). Prentice Hall.
14. Tanner, M. A. (1996). Tools for statistical inference. Springer. 3 edition.
15. Tong, S., & Koller, D. (2000). Active learning for parameter estimation in bayesian network. Neural Information Processing Systems (NIPS).
16. Zhang, Y., & Callan, J. (2001). Maximum likelihood estimation for filtering thresholds. Proceedings of the 24th ACM SIGIR Conference.