Bayesian learning in multi-layer perceptron neural network using Monte Carlo: mlp-mc-1

Abstract

A Bayesian implementation of learning in neural networks using Monte Carlo sampling has been developed by Neal (1996). This computation intensive method has shown encouraging performance in (Neal 1996) and in a study using several datasets in (Rasmussen 1996). For a full description of the method the reader is referred to (Neal 1996). Here a brief… (More)

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