Committee-Based Sample Selection for Probabilistic Classifiers

Journal of Artificial Intelligence Research

Volumn 11, Issue , 1999, Pages 335-360

  Argamon Engelson, Shlomo ^a   Dagan, Ido ^b  

^a Jerusalem College   (Israel)

^b BAR ILAN UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0242445747     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.612     Document Type: Article

References (35)

1. Angluin, D. (1987). Learning regular sets from queries and counterexamples. Information and Computation, 75(2), 87-106.
2. Angluin, D. (1988). Queries and concept learning. Machine Learning, 2, 319-342.
3. Baum, L. E. (1972). An inequality and an associated maximization technique in statistical estimation of probabilistic functions of a markov process. Inequalities, 3:1-8.
4. Black, E., Jelinek, F., Lafferty, J., Magerman, D., Mercer, R., & Roukos, S. (1993). Towards history-based grammars: using richer models for probabilistic parsing. In Proc. of the Annual Meeting of the ACL, pp. 31-37.
5. Brill, E. (1992). A simple rule-based part of speech tagger. In Proc. of ACL Conference on Applied Natural Language Processing.
6. Church, K. W. (1988). A stochastic parts program and noun phrase parser for unrestricted text. In Proc. of ACL Conference on Applied Natural Language Processing.
7. Cohn, D., Atlas, L., & Ladner, R. (1994). Improving generalization with active learning. Machine Learning, 15, 201-221.
8. Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1995). Active learning with statistical models. In Tesauro, G., Touretzky, D., & Alspector, J. (Eds.), Advances in Neural Information Processing, Vol. 7. Morgan Kaufmann, San Mateo, CA.
9. Dagan, I., & Engelson, S. (1995). Committee-based sampling for training probabilistic classifiers. In Proceedings of the International Conference on Machine Learning.
10. Elworthy, D. (1994). Does Baum-Welch re-estimation improve taggers?. In Proc. of ACL Conference on Applied Natural Language Processing, pp. 53-58.
11. Engelson, S., & Dagan, I. (1996a). Minimizing manual annotation cost in supervised learning from corpora. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics.
12. Engelson, S., & Dagan, I. (1996b). Sample selection in natural language learning. In Wermter, S., Riloff, E., & Scheler, G. (Eds.), Symbolic, Connectionist, and Statistical Approaches To Learning For Natural Language Processing. Springer-Verlag.
13. Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the Query By Committee algorithm. Machine Learning, 28, 133-168.
14. Freund, Y. (1994). Sifting informative examples from a random source. In Working Notes of the Workshop on Relevance, AAAI Fall Symposium Series, pp. 85-89.
15. Gale, W., Church, K., & Yarowsky, D. (1993). A method for disambiguating word senses in a large corpus. Computers and the Humanities, 26, 415-439.
16. Johnson, N. L. (1970). Continuous Univariate Distributions - 2. John Wiley & Sons, New York.
17. Johnson, N. L. (1972). Continuous Multivariate Distributions. John Wiley & Sons, New York.
18. Kupiec, J. (1992). Robust part-of-speech tagging using a hidden makov model. Computer Speech and Language, 6, 225-242.
19. Lewis, D. D., & Catlett, J. (1994). Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the International Conference on Machine Learning.
20. Lewis, D. D., & Gale, W. A. (1994). A sequential algorithm for training text classifiers. In Proceedings of the of the A CM SIGIR Conference.
21. Liere, R., & Tadepalli, P. (1997). Active learning with committees for text categorization. In Proceedings of the National Conference on Artificial Intelligence.
22. Littlestone, N. (1988). Learning quickly when irrelevant features abound: A new linear-threshold algorithm. Machine Learning, 2.
23. MacKay, D. J. C. (1992a). The evidence framework applied to classification networks. Neural Computation, 4.
24. MacKay, D. J. C. (1992b). Information-based objective functions for active data selection. Neural Computation, 4.
25. Matan, O. (1995). On-site learning. Tech. rep. LOGIC-95-4, Stanford University.
26. McCallum, A. K., & Nigam, K. (1998). Employing EM and pool-based active learning for text classification. In Proceedings of the International Conference on Machine Learning.
27. Merialdo, B. (1991). Tagging text with a probabilistic model. In Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing.
28. Merialdo, B. (1994). Tagging text with a probabilistic model. Computational Linguistics, 20(2), 155-172.
29. Mitchell, T. (1982). Generalization as search. Artificial Intelligence, 18.
30. Pereira, F., Tishby, N., & Lee, L. (1993). Distributional clustering of english words. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).
31. Plutowski, M., & White, H. (1993). Selecting concise training sets from clean data. IEEE Trans, on Neural Networks, 4(2).
32. Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1988). Numerical Recipes in C. Cambridge University Press.
33. Rabiner, L. R. (1989). A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. of the IEEE, 77(2).
34. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the ACM Workshop on Computational Learning Theory.
35. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Trans. Informat. Theory, T-13.