Boltzmann Machine Learning with the Latent Maximum Entropy Principle
Machine Learning
2012-12-12 v1 Machine Learning
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.
Cite
@article{arxiv.1212.2514,
title = {Boltzmann Machine Learning with the Latent Maximum Entropy Principle},
author = {Shaojun Wang and Dale Schuurmans and Fuchun Peng and Yunxin Zhao},
journal= {arXiv preprint arXiv:1212.2514},
year = {2012}
}
Comments
Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)