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36

ICPR
2008
IEEE

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Improving Bayesian Network parameter learning using constraints

14 years 5 months ago

Improving Bayesian Network parameter learning using constraints

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This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is general in the sense that any convex constraint is allowed, which includes many proposals in the literature. Driven by a maximum entropy criterion and the Imprecise Dirichlet Model, we present a constrained convex optimization formulation to combine priors, constraints and data. Experiments indicate beneﬁts of this framework.

Cassio Polpo de Campos, Qiang Ji

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Computer Vision | Constrained Convex Optimization | ICPR 2008 | Imprecise Dirichlet Model | Maximum Entropy Criterion |

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Added	30 May 2010
Updated	30 May 2010
Type	Conference
Year	2008
Where	ICPR
Authors	Cassio Polpo de Campos, Qiang Ji

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