Uncertainty in Artificial Intelligence
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Boltzmann Machine Learning with the Latent Maximum Entropy Principle
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
Abstract:
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.
Keywords:
Pages: 567-574
PS Link:
PDF Link: /papers/03/p567-wang.pdf
BibTex:
@INPROCEEDINGS{Wang03,
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"
}


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