ABSTRACT
Describes two artificial neural network architectures for constructing maximum entropy models using multinomial distributions. The architectures presented maximize entropy in two ways: by the use of the partition function (which involves the solution of simultaneous polynomial equations), and by constrained gradient ascent. Results comparing the convergence properties of these two architectures are presented. The practical use of these two architectures as a method of inference is illustrated by an application to the prediction of metastases in early breast cancer patients. To assess the predictive accuracy of the maximum entropy models, we compared the results with those obtained by the use of the multilayer perceptron and the probabilistic neural network.
