11 search results - page 2 / 3 » Approximation of hidden Markov models by mixtures of experts... |
CVPR
2003
IEEE
14 years 7 months ago
2003
IEEE
In many applications of graphical models arising in computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. There...
ICML
2010
IEEE
13 years 6 months ago
2010
IEEE
Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn...
CVPR
2005
IEEE
14 years 7 months ago
2005
IEEE
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
ISM
2008
IEEE
13 years 11 months ago
2008
IEEE
Many multimedia applications rely on the computation of logarithms, for example, when estimating log-likelihoods for Gaussian Mixture Models. Knowing of the demand to compute loga...
ICML
2008
IEEE
14 years 6 months ago
2008
IEEE
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...