697 search results - page 53 / 140 » Learning a Hidden Hypergraph |
NN
1997
Springer
15 years 1 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...
ISCI
2008
14 years 9 months ago
2008
In this paper, two modified constrained learning algorithms are proposed to obtain better generalization performance and faster convergence rate. The additional cost terms of the ...
JMLR
2010
14 years 4 months ago
2010
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
ISCAS
2005
IEEE
15 years 3 months ago
2005
IEEE
— Cell assembly is one of explanations of information processing in the brain, in which an information is represented by a firing space pattern of a group of plural neurons. On ...
IJCAI
1997
14 years 11 months ago
1997
cal maps provide a useful abstraction for robotic navigation and planning. Although stochastic mapscan theoreticallybe learned using the Baum-Welch algorithm,without strong prior ...