Using feature hierarchies in Bayesian network learning (extended abstract)

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 1864, Issue , 2000, Pages 260-270

  DesJardins, Marie ^a   Getoor, Lise ^b   Koller, Daphne ^b  

^a SRI INTERNATIONAL   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84942916008     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-44914-0_16     Document Type: Conference Paper

References (15)

1. M. A. Behr, M. A. Wilson, W. P. Gill, H. Salamon, G. K. Schoolnik, S. Rane, and P. M. Small. Comparative genomics of BCG vaccines by whole genome DNA microarray. Science, 284:1520-23, 1999.
2. Craig Boutilier, Nir Friedman, Moises Goldszmidt, and Daphne Koller. Contextspecific independence in Bayesian networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), pages 115-123, August 1996.
3. D. M. Chickering. Learning Bayesian networks is NP-complete. In D. Fisher and H.-J. Lenz, editors, Learning from Data: Artificial Intelligence and Statistics V. Springer Verlag, 1996.
4. D. M. Chickering, D. Heckerman, and C. Meek. A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 80-89, 1997.
5. G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347, 1992.
6. Nir Friedman and Moises Goldszmidt. Discretizing continuous attributes while learning Bayesian networks. In Proceedings of the Thirteenth International Conference on Machine Learning, 1996.
7. Nir Friedman and Moises Goldszmidt. Learning Bayesian networks with local structure. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), 1996.
8. D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan, editor, Learning in Graphical Models. Kluwer, Dordrecht, Netherlands, 1998.
9. D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
10. W. Lam and F. Bacchus. Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence, 10:269-293, 1994.
11. Stefano Monti and Gregory F. Cooper. A latent variable model for multivariate discretization. In Proceedings of the Seventh International Workshop on AI & Statistics (Uncertainty 99), 1999.
12. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, 1988.
13. D. J. Spiegelhalter, A. P. Dawid, S. L. Lauritzen, and R. G. Cowell. Bayesian analysis in expert systems. Statistical Science, 8:219-283, 1993.
14. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. Number 81 in Lecture Notes in Statistics. Springer-Verlag, NY, 1993.
15. Michael P. Wellman and Chao-Lin Liu. State-space abstraction for anytime evaluation of probabilistic networks. In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI-93), pages 567-574, 1994.