219 search results - page 21 / 44 » Learning the Dimensionality of Hidden Variables |
122
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UAI
1998
15 years 1 months ago
1998
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
CVPR
2007
IEEE
16 years 1 months ago
2007
IEEE
Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper, we develop a discriminative framework for simultaneous se...
GECCO
2007
Springer
15 years 5 months ago
2007
Springer
In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
109
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IJDAR
2010
14 years 10 months ago
2010
Inthispaper,weproposeadescriptorcombination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This ...
NN
1997
Springer
15 years 3 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...