Probabilistic learning on a neural network

This paper presents an information theoretic description of a possible learning mechanism for an adaptive signal processing system. It is shown that this learning system, when applied to a multilayered perceptron, produces a learning algorithm which closely resembles the conventional generalised delta rule, but treats the outputs of the network wholly as probabilities