Carsten Peterson and James R. Anderson
A mean field theory learning algorithm for neural networks
Complex Systems 1, 995-1019 (1987)
Based on the Boltzmann Machine concept, we derive a learning algorithm in which time-consuming stochastic measurements of correlations are replaced by solutions to deterministic mean field theory equations. The method is applied to the XOR (exclusive-or), encoder, and line symmetry problems with substantial success. We observe speedup factors ranging from 10 to 30 for these applications and a significantly better learning performance in general.
LU TP 87-01