Boltzmann Machine Learning with the Latent Maximum Entropy Principle
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
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.
PDF Link: /papers/03/p567-wang.pdf
AUTHOR = "Shaojun Wang
and Dale Schuurmans and Fuchun Peng and Yunxin Zhao",
TITLE = "Boltzmann Machine Learning with the Latent Maximum Entropy Principle",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "567--574"