Learning Bayesian Networks Does Not Have to Be NP-Hard-Reference-Cited by-同舟云学术

Learning Bayesian Networks Does Not Have to Be NP-Hard

Published:2006 Issue: Volume: Page:305-314
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Dojer Norbert

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11821069_27.pdf

Reference10 articles.

1. Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data: Artificial Inteligence and Statistics V. Springer, Heidelberg (1996)

2. Chickering, D.M., Heckerman, D., Meek, C.: Large-sample learning of Bayesian networks is NP-hard. Journal of Machine Learning Research 5, 1287–1330 (2004)

3. Dasgupta, S.: Learning polytrees. In: Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1999), pp. 134–141. Morgan Kaufmann Publishers, San Francisco (1999)

4. Meek, C.: Finding a path is harder than finding a tree. J. Artificial Intelligence Res. 15, 383–389 (2001) (electronic)

5. Ott, S., Imoto, S., Miyano, S.: Finding optimal models for small gene networks. In: Pac. Symp. Biocomput., pp. 557–567 (2004)

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