Explorations of the mean field theory learning algorithm

-The mean field theory ( MFT) learning algorithm is" elaborated and explored with respect to a variety of tasks. MFT is benchmarked against the back-propagation learning algorithm ( BP) on two different.feature recognition problems: two-dimensional mirror symmetry and multidimensional statistical pattern classification. We find that while the two algorithms are very similar with respect to generalization properties, MFT normally requires a substantially smaller number of training epochs than BP. Since the MFT model is bidirectional, rather than Jeed-forward, its use can be extended naturally from purely ,functional mappings to a content addressable memory. A network with N visible and N hidden units can store up to approximately 4N patterns with good content-addressabilio'. We stress an implementational advantage for MFT: it is natural /br VLSI circuitry. Keywords--Neural network, Bidirectional, Generalization, Content addressable memory, Mean field theory, Learning algorithm.