ECSQARU 1999: Symbolic and Quantitative Approaches to Reasoning and Uncertainty pp 221-232 | Cite as
Optimized Algorithm for Learning Bayesian Network from Data
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Abstract
In this paper, we present an algorithm for learning the most probable structure of a Bayesian Network from a database of cases. Starting from two previous algorithms, K2 of Cooper and Herskovits, and B of Buntime, we developed a new algorithm that relaxes the assumption of total ordering on the nodes needed by K2 and has less computations than B. To improve our algorithm, we added some heuristics and an interactive process with the user.
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© Springer-Verlag Berlin Heidelberg 1999