Request for Boltzmann Machine information

[Terry Sejnowski <terry@cs.jhu.edu>]

At the recent N'Euro 88 meeting in Paris, R. L. Chrisly working
with T. Kohonen reported results from Boltzmann learning,
back-propagation, and Kohonen's vector quantization applied
to a classification problem in a noisy environment.
Boltzmann produced classifications close to the optimal
Bayes strategy; Back-prop did well for small problems, but
the perfomrance deteriorated for large problems and was overtaken
by Kohonen's network.  Boltzmann, however, took much more
time to learn and was sensitive to the input representation
(a unary code for continuous value inputs was better than
an analog code).  For more information write to Kohonen's
lab in Finland.  Kohonen mentioned to me recently that a 
two level Kohonen net has significantly improved performance.

The reason that Boltmann machines do well on statistical
inference problems is that they are in principle capable of
optimal Baysian inference.  See Proc. IEEE Conference on
Computer Vision and Pattern Recognition, Washington, DC,
June 1983.  Hinton & Sejnowski:  Optimal Perceptual Inference.

Terry

-----