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» Estimating Likelihoods for Topic Models
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ICML
2003
IEEE
16 years 16 days ago
Learning Mixture Models with the Latent Maximum Entropy Principle
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 ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
ICPR
2008
IEEE
15 years 6 months ago
Analytical method for MGRF Potts model parameter estimation
This paper proposes a new analytical method for estimating parameters of a homogeneous isotropic Potts model with an asymmetric Gibbs potential function. The model is generalized ...
Asem M. Ali, Aly A. Farag, Georgy L. Gimel'farb
JMLR
2010
165views more  JMLR 2010»
14 years 6 months ago
Learning with Blocks: Composite Likelihood and Contrastive Divergence
Composite likelihood methods provide a wide spectrum of computationally efficient techniques for statistical tasks such as parameter estimation and model selection. In this paper,...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
ICASSP
2010
IEEE
14 years 12 months ago
A novel estimation of feature-space MLLR for full-covariance models
In this paper we present a novel approach for estimating featurespace maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maxim...
Arnab Ghoshal, Daniel Povey, Mohit Agarwal, Pinar ...
ICA
2004
Springer
15 years 5 months ago
Blind Maximum Likelihood Separation of a Linear-Quadratic Mixture
Abstract. We proposed recently a new method for separating linearquadratic mixtures of independent real sources, based on parametric identification of a recurrent separating struc...
Shahram Hosseini, Yannick Deville