Learning with mixtures of trees

Journal of Machine Learning Research

Volumn 1, Issue 1, 2001, Pages 1-48

  Meilǎ, Marina ^a   Jordan, Michael I ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 24044550075     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (51)

1. Bishop, C. M. (1999). Latent variable models. In M. I. Jordan (Ed.), Learning in Graphical Models. Cambridge, MA: MIT Press.
2. Blake, C., & Merz, C. (1998). UCI Repository of Machine Learning Databases. http://www.ics.uci.edu/∼mlearn/MLRepository.html.
3. Boutilier, C., Friedman, N., Goldszmidt, M., & Koller, D. (1996). Context-specific independence in Bayesian networks. In Proceedings of the 12th Conference on Uncertainty in AI (pp. 64-72). Morgan Kaufmann.
4. Buntine, W. (1996). A guide to the literature on learning graphical models. IEEE Transactions on Knowledge and Data Engineering, 8, 195-210.
5. Cheeseman, P., & Stutz, J. (1995). Bayesian classification (AutoClass): Theory and results. In U. Fayyad, G. Piatesky-Shapiro, P. Smyth, & Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 153-180). AAAI Press.
6. Cheng, J., Bell, D. A., & Liu, W. (1997). Learning belief networks from data: an information theory based approach. In Proceedings of the Sixth ACM International Conference on Information and Knowledge Management.
7. Chow, C. K., & Liu, C. N. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, IT-14(3), 462-467.
8. Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
9. Cormen, T. H., Leiserson, C. E., & Rivest, R. R. (1990). Introduction to Algorithms. Cambridge, MA: MIT Press.
10. Cowell, R. G., Dawid, A. P., Lauritzen, S. L., & Spiegelhalter, D. J. (1999). Probabilistic Networks and Expert Systems. New York, NY: Springer.
11. Dayan, P., & Zemel, R. S. (1995). Competition and multiple cause models. Neural Computation, 7(3), 565-579.
12. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B, 39, 1-38.
13. Fredman, M. L., & Tarjan, R. E. (1987). Fibonacci heaps and their uses in improved network optimization algorithms. Journal of the Association for Computing Machinery, 34(3), 596-615.
14. Frey, B. J., Hinton, G. E., & Dayan, P. (1996). Does the wake-sleep algorithm produce good density estimators? In D. Touretzky, M. Mozer, & M. Hasselmo (Eds.), Neural Information Processing Systems (pp. 661-667). Cambridge, MA: MIT Press.
15. Friedman, N. (1998). The Bayesian structural EM algorithm. In Proceedings of the 14th Conference on Uncertainty in AI (pp. 129-138). San Francisco, CA: Morgan Kaufmann.
16. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131-163.
17. Friedman, N., & Getoor, L. (1999). Efficient learning using constrained sufficient statistics. In Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics (AISTATS-99).
18. Friedman, N., Getoor, L., Koller, D., & Pfeffer, A. (1996). Learning probabilistic relational models. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI) (pp. 1300-1307).
19. Friedman, N., Goldszmidt, M., & Lee, T. (1998). Bayesian network classification with continous attributes: Getting the best of both discretization and parametric fitting. In Proceedings of the International Conference on Machine Learning (ICML).
20. Geiger, D. (1992). An entropy-based learning algorithm of Bayesian conditional trees. In Proceedings of the 8th Conference on Uncertainty in AI (pp. 92-97). Morgan Kaufmann Publishers.
21. Geiger, D., & Heckerman, D. (1996). Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence, 82, 45-74.
22. Hastie, T., & Tibshirani, R. (1996). Discriminant analysis by mixture modeling. Journal of the Royal Statistical Society B, 58, 155-176.
23. Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20(3), 197-243.
24. Hinton, G. E., Dayan, P., Frey, B., & Neal, R. M. (1995). The wake-sleep algorithm for unsupervised neural networks. Science, 268, 1158-1161.
25. Jelinek, F. (1997). Statistical Methods for Speech Recognition. Cambridge, MA: MIT Press.
26. Jordan, M. I., & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214.
27. Kontkanen, P., Myllymaki, P., & Tirri, H. (1996). Constructing Bayesian finite mixture models by the EM algorithm (Tech. Rep. No. C-1996-9). University of Helsinki, Department of Computer Science.
28. Lauritzen, S. L. (1995). The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis, 19, 191-201.
29. Lauritzen, S. L. (1996). Graphical Models. Oxford: Clarendon Press.
30. Lauritzen, S. L., Dawid, A. P., Larsen, B. N., & Leimer, H.-G. (1990). Independence properties of directed Markov fields. Networks, 20, 579-605.
31. MacLachlan, G. J., & Bashford, K. E. (1988). Mixture Models: Inference and Applications to Clustering. NY: Marcel Dekker.
32. Meilǎ, M., & Jaakkola, T. (2000). Tractable Bayesian learning of tree distributions. In C. Boutilier & M. Goldszmidt (Eds.), Proceedings of the 16th Conference on Uncertainty in AI (pp. 380-388). San Francisco, CA: Morgan Kaufmann.
33. Meilǎ, M., & Jordan, M. I. (1998). Estimating dependency structure as a hidden variable. In M. I. Jordan, M. J. Kearns, & S. A. Solla (Eds.), Neural Information Processing Systems (pp. 584-590). MIT Press.
34. Meilǎ-Predoviciu, M. (1999). Learning with mixtures of trees. Unpublished doctoral dissertation, Massachusetts Institute of Technology.
35. Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine Learning, Neural and Statistical Classification. New York: Ellis Horwood.
36. Monti, S., & Cooper, G. F. (1998). A Bayesian network classfier that combines a finite mixture model and a naive Bayes model (Tech. Rep. No. ISSP-98-01). University of Pittsburgh.
37. Moore, A. W., & Lee, M. S. (1998). Cached sufficient statistics for efficient machine learning with large datasets. Journal for Artificial Intelligence Research, 8, 67-91.
38. Neal, R. M., & Hinton, G. E. (1999). A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan (Ed.), Learning in Graphical Models (pp. 355-368). Cambridge, MA: MIT Press.
39. Ney, H., Essen, U., & Kneser, R. (1994). On structuring probabilistic dependences in stochastic language modelling. Computer Speech and Language, 8, 1-38.
40. Noordewier, M. O., Towell, G. G., & Shavlik, J. W. (1991). Training knowledge-based neural networks to recognize genes in DNA sequences. In R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), Advances in Neural Information Processing Systems (pp. 530-538). Morgan Kaufmann Publishers.
41. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufman Publishers.
42. Philips, P., Moon, H., Rauss, P., & Rizvi, S. (1997). The FERET evaluation methodology for face-recognition algorithms. In Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico.
43. Rasmussen, C. E., Neal, R. M., Hinton, G. E., Camp, D. van, Revow, M., Ghahramani, Z., Kustra, R., & Tibshrani, R. (1996). The DELVE Manual. http://www.cs.utoronto.ca/∼delve.
44. Rissanen, J. (1989). Stochastic Complexity in Statistical Inquiry. New Jersey: World Scientific Publishing Company.
45. Rubin, D. B., & Thayer, D. T. (1983). EM algorithms for ML factor analysis. Psychometrika, 47, 69-76.
46. Saul, L. K., & Jordan, M. I. (1999). A mean field learning algorithm for unsupervised neural networks. In M. I. Jordan (Ed.), Learning in Graphical Models (pp. 541-554). Cambridge, MA: MIT Press.
47. Shafer, G., & Shenoy, P. (1990). Probability propagation. Annals of Mathematics and Artificial Intelligence, 2, 327-352.
48. Smyth, P., Heckerman, D., & Jordan, M. I. (1997). Probabilistic independence networks for hidden Markov probability models. Neural Computation, 9, 227-270.
49. Thiesson, B., Meek, C., Chickering, D. M., & Heckerman, D. (1997). Learning mixtures of Bayes networks (Tech. Rep. Nos. MSR-POR-97-30). Microsoft Research.
50. Watson, J. D., Hopkins, N. H., Roberts, J. W., Steitz, J. A., &; Weiner, A. M. (1987). Molecular Biology of the Gene (Vol. I, 4 ed.). Menlo Park, CA: The Benjamin/Cummings Publishing Company.
51. West, D. B. (1996). Introduction to Graph Theory. Upper Saddle River, NJ: Prentice Hall.