396 search results - page 8 / 80 » On the Use of Restrictions for Learning Bayesian Networks |
ENC
2004
IEEE
13 years 11 months ago
2004
IEEE
A method to induce bayesian networks from data to overcome some limitations of other learning algorithms is proposed. One of the main features of this method is a metric to evalua...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we e...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating an...
NECO
2008
13 years 7 months ago
2008
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
CVPR
2000
IEEE
14 years 9 months ago
2000
IEEE
Design and development of novel human-computer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous mu...