Neural network classifiers under changing a priori conditions

Once a multilayer perceptron is trained on labelled examples, it is applied to classify new patterns. If the a priori class probabilities of the new patterns are changed, the network becomes suboptimal for its task. The Letter shows how to obtain a posteriori class probabilities under the new circumstances, without the need for a new supervised training session.