PROCEEDINGS IPMU '08
Competing Fusion for Bayesian Applications
Michael Abramovici, Manuel Neubach, Madjid Fathi, Alexander Holland.
In this paper we address and discuss the
problem of learning graphical models
like Bayesian networks using structure
learning algorithms. We present a new
parameterized structure learning
approach. A competing fusion
mechanism to aggregate expert
knowledge stored in distributed
knowledge bases or probability
distributions is also described.
Experimental results of a medical case
study show that our approach can
improve the quality of the learned
graphical model.
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