Mixtures of Conditional Maximum Entropy Models

Proceedings, Twentieth International Conference on Machine Learning

Volumn 2, Issue , 2003, Pages 584-591

  Pavlov, Dmitry ^a   Popescul, Alexandrin ^b   Pennock, David M ^c   Ungar, Lyle H ^b  

^a YAHOO INC   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c Overture Services Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA REDUCTION; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; STATISTICAL METHODS;

BAYESIAN STATISTICS; LATENT STRUCTURE; MAXIMUM ENTROPY; MIXTURE MODELS;

ENTROPY;

EID: 1942420722     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Baker, L. D., & McCallum, A. (1998). Distributional clustering of words for text classification. Proceedings of the 21st ACM International Conference on Research and Development in Information Retrieval (SIGIR-98) (pp. 96-103). ACM Press, New York.
2. Berger, A., Della Pietra, S., & Della Pietra, V. (1996). A maximum entropy approach to natural language processing. Computational Linguistics, 22, 39-72.
3. Blake, C., & Merz, C. (1998). UCI repository of machine learning databases.
4. Buehler, E. C., & Ungar, L. H. (2001). Maximum entropy methods for biological sequence modeling. BIOKDD (pp. 60-64).
5. Chen, S., & Rosenfeld, R. (1999). A Gaussian prior for smoothing maximum entropy models (Technical Report CMUCS-99-108). Carnegie Mellon University.
6. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., & Slattery, S. (1998). Learning to extract symbolic knowledge from the World Wide Web. Proceedings of 15th Conference of the American Association for Artificial Intelligence (AAAI-98) (pp. 509-516). AAAI Press, Menlo Park.
7. Darroch, J. N., & Ratcliff, D. (1972). Generalized iterative scaling for log-linear models. Annals of Mathematical Statistics, 43, 1470-1480.
8. David, R., & Kenneth, T. (1998). Mixed logit with repeated choices: Households' choices of appliance efficiency level. Review of Economics and Statistics, 80, 1-11.
9. Della Pietra, S., Della Pietra, V., & Lafferty, J. (1997). Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 380-393.
10. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1-38.
11. Goodman, J. (2002). Sequential conditional generalized iterative scaling. Association for Computational Linguistics Annual Meeting.
12. Jaakkola, T., Meila, M., & Jebara, T. (1999). Maximum entropy discrimination (Technical Report MIT AITR-1668).
13. Jaynes, E. T. (1979). Where do we stand on maximum entropy? The Maximum Entropy Formalism (pp. 15-118). Cambridge MA: MIT Press.
14. Jelinek, F. (1998). Statistical methods for speech recognition. Cambridge. MA:MIT Press.
15. King, R., Feng, C., & Sutherland, A. (1995). Stat-Log: Comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence, 9(3), 289-333.
16. Lim, T.-S., Loh, W.-Y., & Shih, Y.-S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40, 203-228.
17. McFadden, D., & Train, K. (1997). Mixed MNL models for discrete response (Technical Report Department of Economics, UC Berkeley.).
18. McLachlan, G., & Basford, K. (1988). Mixture models. Marcel Dekker, New York.
19. McLachlan, G., & Krishnan, T. (1997). The EM algorithm and extensions. John Wiley and Sons, New York.
20. Nigam, K., Lafferty, J., & McCallum, A. (1999). Using maximum entropy for text classification. IJCAI-99 Workshop on Machine Learning for Information Filtering (pp. 61-67).
21. Nigam, K., Popescul, A., & McCallum, A. (2000, unpublished commercial project). Using latent structure of a document collection to improve text classification. Whizbang! Labs, Pittsburgh.
22. Pavlov, D., & Pennock, D. (2002). A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains. Proceedings of Neural Information Processing Systems (NIPS-2002), to appear.
23. Pavlov, D., & Smyth, P. (2001). Probabilistic query models for transaction data. Proceedings of Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 164-173). New York, NY: ACM Press.
24. Pereira, F., Tishby, N., & Lee, L. (1993). Distributional clustering of English words. Meeting of the Association for Computational Linguistics (pp. 183-190).
25. Ratnaparkhi, A. (1996). A maximum entropy model for part-of-speech tagging. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 133-142. Somerset, New Jersey: Association for Computational Linguistics.
26. Wang, S., Rosenfeld, R., Zhao, Y., & Shuurmans, D. (2002). The latent maximum entropy principle. IEEE International Symposium on Information Theory (ISIT).
27. Wolfinger, R., & O'Connell, M. (1993). Generalized linear mixed models: A pseudo-likelihood approach. Journal of Statistical Computation and Simulation, 48, 233-243.