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ADMA
2006
Springer

A New Polynomial Time Algorithm for Bayesian Network Structure Learning

14 years 5 months ago
A New Polynomial Time Algorithm for Bayesian Network Structure Learning
We propose a new algorithm called SCD for learning the structure of a Bayesian network. The algorithm is a kind of constraintbased algorithm. By taking advantage of variable ordering, it only requires polynomial time conditional independence tests and learns the exact structure theoretically. A variant which adopts the Bayesian Dirichlet scoring function is also presented for practical purposes. The performance of the algorithms are analyzed in several aspects and compared with other existing algorithms. In addition, we define a new evaluation metric named EP power which measures the proportion of errors caused by previously made mistakes in the learning sequence, and use the metric for verifying the robustness of the proposed algorithms.
Sanghack Lee, Jihoon Yang, Sungyong Park
Added 13 Jun 2010
Updated 13 Jun 2010
Type Conference
Year 2006
Where ADMA
Authors Sanghack Lee, Jihoon Yang, Sungyong Park
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