32 search results - page 4 / 7 » Learning Mixture Models with the Latent Maximum Entropy Prin... |
NIPS
2008
13 years 9 months ago
2008
Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative m...
ECML
2005
Springer
14 years 1 months ago
2005
Springer
Abstract. In this paper we consider latent variable models and introduce a new U-likelihood concept for estimating the distribution over hidden variables. One can derive an estimat...
IJAR
2010
13 years 6 months ago
2010
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing ...
NIPS
2007
13 years 9 months ago
2007
The expectation maximization (EM) algorithm is a widely used maximum likelihood estimation procedure for statistical models when the values of some of the variables in the model a...
VLSISP
1998
13 years 7 months ago
1998
This paper presents an adaptive structure self-organizing finite mixture network for quantification of magnetic resonance (MR) brain image sequences. We present justification fo...