%0 Journal Article
%D 2004
%T Learning Mixture Models with the Regularized Latent Maximum Entropy Principle
%A D. Schuurmans
%A F. Peng
%A Y. Zhao
%A Shaojun Wang
%X We present a new approach to estimating mixture models 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 mixture model estimation, and show how robust new variants 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 latent variable models from small amounts of data.
%G eng
%0 Conference Paper
%B Learning Mixture Models with the Latent Maximum Entropy Principle
%D 2003
%T Learning Mixture Models with the Latent Maximum Entropy Principle
%A Y. Zhao
%A Shaojun Wang
%A F. Peng
%A D. Schuurmans
%B Learning Mixture Models with the Latent Maximum Entropy Principle
%G eng
%0 Conference Paper
%B The Latent Maximum Entropy Principle
%D 2002
%T The Latent Maximum Entropy Principle
%A Shaojun Wang
%A Y. Zhao
%A D. Schuurmans
%A R. Rosenfeld
%B The Latent Maximum Entropy Principle
%G eng
%0 Conference Paper
%D 2001
%T Latent Maximum Entropy Principle for Statistical Language Modeling
%A Shaojun Wang
%A Y. Zhao
%A R. Rosenfeld
%G eng