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IJPRAI
1998

Obtaining The Correspondence between Bayesian and Neural Networks

13 years 9 months ago
Obtaining The Correspondence between Bayesian and Neural Networks
We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we can obtain a correspondence between the two networks by deriving a closed-form solution so that the outputs of the two networks are similar in the least square error sense, not only when determining the discriminant function, but for the full range of their outputs. For this purpose we take into consideration the probability density functions of the independent variables of the problem when we compute the least square error approximation. Our methodology is demonstrated with the help of some real data concerning the problem of risk of desertification assessment for some burned forests in Attica, Greece where the parameters of the Bayesian network constructed for this task are successfully estimated given a neural network trained with a set of data.
Athena Stassopoulou, Maria Petrou
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1998
Where IJPRAI
Authors Athena Stassopoulou, Maria Petrou
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