Tech Report Announcement

[Eric Hartman]

The following MCC Technical Report is now available.

Requests may be sent to

 eric at mcc.com 

or

 Eric Hartman
 Microelectronics and Computer Technology Corporation
 3500 West Balcones Center Drive
 Austin, TX 78759-6509
 U.S.A.

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      Explorations of the Mean Field Theory Learning Algorithm

                Carsten Peterson* and Eric Hartman 

        Microelectronics and Computer Technology Corporation
                 3500 West Balcones Center Drive
                      Austin, TX 78759-6509

            MCC Technical Report Number: ACA-ST/HI-065-88

                            Abstract:

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 eight-dimensional
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 feed-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 2N patterns with good content-addressability.  
We stress an implementational advantage for MFT: it is natural for VLSI 
circuitry. Also, its inherent parallelism can be exploited with fully 
synchronous updating, allowing efficient simulations on SIMD architectures.

*Present Address: Department of Theoretical Physics 
                  University of Lund 
                  Solvegatan 14A, S-22362 Lund, Sweden