On the generalization ability of neural network classifiers

Summary form only given, as follows. An approach for evaluation of the generalization ability of neural network classifiers is discussed, and a minimum error neural network (MENN) classifier is offered. A probabilistic model and an estimation scheme for the input distribution have been defined. After defining and minimizing an error function for the classifier output, a criterion for the MENN classifier was found. The expected MENN classifier performance was then evaluated. It has been shown that the boundaries of the MENN decision surface, in the sense of least mean square error, are equivalent to the boundaries obtained by the Bayes rule. The proposed technique can be used to evaluate the generalization ability of any supervised learning classifier. >