276 search results - page 9 / 56 » PAC-Learning of Markov Models with Hidden State |
146
Voted
ICIP
2007
IEEE
15 years 10 months ago
2007
IEEE
In this paper, we propose a general two-dimensional hidden Markov model (2D-HMM), where dependency of the state transition probability on any state is allowed as long as causality...
118
Voted
ICPR
2008
IEEE
15 years 10 months ago
2008
IEEE
One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty ...
140
Voted
EMNLP
2008
15 years 5 months ago
2008
There is growing interest in applying Bayesian techniques to NLP problems. There are a number of different estimators for Bayesian models, and it is useful to know what kinds of t...
133
Voted
ICPR
2000
IEEE
16 years 4 months ago
2000
IEEE
We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. First, an image can be segmented in a way that best matches its stati...
197
Voted
ICIP
2001
IEEE
16 years 5 months ago
2001
IEEE
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...