Learning Recursive Probability Trees from Data

Author:

Cano Andrés,Gómez-Olmedo Manuel,Moral Serafín,Pérez-Ariza Cora Beatriz,Salmerón Antonio

Publisher

Springer Berlin Heidelberg

Reference6 articles.

1. Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Horvitz, E., Jensen, F.V. (eds.) Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, pp. 115–123. Morgan Kaufmann (1996)

2. Cano, A., Gómez-Olmedo, M., Moral, S., Pérez-Ariza, C., Salmerón, A.: Learning recursive probability trees from probabilistic potentials. International Journal of Approximate Reasoning 53, 1367–13875 (2012)

3. Cano, A., Gómez-Olmedo, M., Moral, S., Pérez-Ariza, C., Salmerón, A.: Inference in Bayesian networks with recursive probability trees: data structure definition and operations. International Journal of Intelligent Systems 28 (2013)

4. Heckerman, D., Geiger, D., Chickering, D.: Learning bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)

5. Quinlan, J.: Induction of decision trees. Machine Learning 1, 81–106 (1986)

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1. An Extended Approach to Learning Recursive Probability Trees from Data;International Journal of Intelligent Systems;2015-01-05

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