Obtaining the correspondence between bayesian and neural networks

摘要

We present in this paper a novel method for eliciting the conditional probability ma- trices needed for a Bayesian network with the help of a 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.