A Fast Hill-Climbing Algorithm for Bayesian Networks Structure Learning
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Publication:3524969
DOI10.1007/978-3-540-75256-1_52zbMath1148.68514OpenAlexW1575390667MaRDI QIDQ3524969
Juan L. Mateo, José A. Gámez, José M. Puerta
Publication date: 16 September 2008
Published in: Lecture Notes in Computer Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-540-75256-1_52
Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
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Uses Software
Cites Work
- The max-min hill-climbing Bayesian network structure learning algorithm
- Causation, prediction, and search
- Bayesian network classifiers
- Ant colony optimization for learning Bayesian networks.
- Learning Bayesian networks: The combination of knowledge and statistical data
- 10.1162/153244303321897717
- Symbolic and Quantitative Approaches to Reasoning with Uncertainty
- A hybrid methodology for learning belief networks: BENEDICT
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