A Mean Field Theory Learning Algorithm for Neural Networks
Carsten Peterson
James R. Anderson
Microelectronics and Computer Technology Corporation,
3500 West Balcones Center Drive, Austin, TX 78759-6509, USA
Abstract
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.
