589 search results - page 93 / 118 » Modeling affordances using Bayesian networks |
NIPS
1998
14 years 11 months ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
IJAR
2010
14 years 8 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 ...
IPMU
1992
Springer
15 years 2 months ago
1992
Springer
This paper deals with the problem of inference under uncertain information. This is a generalization of a paper of Cardona et al. (1991a) where rules were not allowed to contain n...
IPPS
2006
IEEE
15 years 4 months ago
2006
IEEE
As an extension of Bayesian network, module network is an appropriate model for inferring causal network of a mass of variables from insufficient evidences. However learning such ...
CVPR
2007
IEEE
16 years 9 hour ago
2007
IEEE
One problem with the adaptive tracking is that the data that are used to train the new target model often contain errors and these errors will affect the quality of the new target...