Title: Revisiting Boltzmann Learning:
       Parameter Estimation in Markov Random Fields
Authors: Lars Kai Hansen, Lars Nonboe Andersen, Ulrik Kjems, and Jan Larsen
Address: CONNECT, Electronics Institute, building 349
         Technical University of Denmark, DK-2800 Lyngby, Denmark
         emails: lkhansen,nonboe,kjems,jlarsen@ei.dtu.dk

Aabstract:
This contribution concerns a generalization of the Boltzmann Machine
that allows us to use the learning rule for a much wider class of
maximum likelihood and maximum a posteriori problems, including both
supervised and unsupervised learning. Furthermore, the approach allows 
us to discuss regularization and generalization in the context 
of Boltzmann Machines.
We provide an illustrative example concerning parameter estimation 
in an inhomogeneous Markov Field.