Learning Bayesian Networks With Local Structure

Learning Bayesian Networks With Local Structure (1996)

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by Nir Friedman , Moises Goldszmidt

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Datum	Value	Source
TITLE	Learning Bayesian Networks With Local Structure	user correction - Legacy Corrections
AUTHOR NAME	Nir Friedman	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Division	SVM HeaderParse 0.2
AUTHOR ADDR	387 Soda Hall, University of California,; Berkeley, CA 94720.	SVM HeaderParse 0.1
AUTHOR NAME	Moises Goldszmidt	SVM HeaderParse 0.1
AUTHOR AFFIL	; SRI International; Abstract.	SVM HeaderParse 0.2
AUTHOR ADDR	; 333 Ravenswood Avenue, EK329, Menlo; Park, CA 94025.	SVM HeaderParse 0.1
ABSTRACT	. We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability distributions (CPDs) that quantify these networks. This increases the space of possible models, enabling the representation of CPDs with a variable number of parameters. The resulting learning procedure induces models that better emulate the interactions present in the data. We describe the theoretical foundations and practical aspects of learning local structures and provide an empirical evaluation of the proposed learning procedure. This evaluation indicates that learning curves characterizing this procedure converge faster, in the number of training instances, than those of the standard procedure, which ignores the local structure of the CPDs. Our results also show that networks learned with local structures tend to be more complex (in terms of a...	user correction - Legacy Corrections
YEAR	1996	user correction - Legacy Corrections
CITATIONS	6 found	ParsCit 1.0