A Method for the Efficient Design of Boltzmann Machines for Classiffication Problems

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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Ajay Gupta, Wolfgang Maass


We introduce a method for the efficient design of a Boltzmann machine (or a Hopfield net) that computes an arbitrary given Boolean function f . This method is based on an efficient simulation of acyclic circuits with threshold gates by Boltzmann machines. As a consequence we can show that various concrete Boolean functions f that are relevant for classification problems can be computed by scalable Boltzmann machines that are guaranteed to converge to their global maximum configuration with high probability after constantly many steps.