  We present a new statistical learning paradigm for Boltzmann machines based
on a new inference principle we have proposed: the latent maximum entropy
principle (LME). LME is different both from Jaynes maximum entropy principle
and from standard maximum likelihood estimation.We demonstrate the LME
principle BY deriving new algorithms for Boltzmann machine parameter
estimation, and show how robust and fast new variant of the EM algorithm can be
developed.Our experiments show that estimation based on LME generally yields
better results than maximum likelihood estimation, particularly when inferring
hidden units from small amounts of data.
