paper announcement

[Lawrence Saul]

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FTP-host: psyche.mit.edu
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The following paper is now available by anonymous ftp:


      Boltzmann Chains and Hidden Markov Models [8 pages]

           Lawrence K. Saul and Michael I. Jordan
       Center for Biological and Computational Learning
           Massachusetts Institute of Technology
                79 Amherst Street, E10-243
		    Cambridge, MA 02139

Abstract:
We propose a statistical mechanical framework for the modeling of
discrete time series.  Maximum likelihood estimation is done via
Boltzmann learning in one-dimensional networks with tied weights.  We
call these networks Boltzmann chains and show that they contain hidden
Markov models (HMMs) as a special case.  Our framework also motivates
new architectures that address particular shortcomings of HMMs.  We
look at two such architectures: parallel chains that model feature
sets with disparate time scales, and looped networks that model
long-term dependencies between hidden states.  For these networks, we
show how to implement the Boltzmann learning rule exactly, in
polynomial time, without resort to simulated or mean-field annealing.
The necessary computations are done by exact decimation procedures
from statistical mechanics.


*** To appear in the NIPS 1994 Proceedings.