Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs

Author:

Acid Silvia,de Campos Luis M.,Castellano Javier G.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference39 articles.

1. Acid, S., & de Campos, L.M. (2001). A hybrid methodology for learning belief networks: Benedict. International Journal of Approximate Reasoning, 27, 235–262.

2. Acid, S., & de Campos, L.M. (2003). Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs. Journal of Artificial Intelligence Research, 18, 445–490.

3. Aliferis, C. F., Tsamardinos, I., & Statnikov, A. (2003). HITON: A novel Markov blanket algorithm for optimal variable selection. In Proceedings of the 2003 American Medical Informatics Association (pp. 21–25).

4. Blake, C. L., & Merz, C. J. (1998). UCI Repository of machine learning databases. http://www.ics.uci.edu/∼mlearn/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sciences.

5. Cheng, J., & Greiner, R. (1999). Comparing Bayesian network classifiers. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 101–108).

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