22 search results - page 4 / 5 » Bayesian Learning in Undirected Graphical Models: Approximat... |
IWINAC
2007
Springer
15 years 5 months ago
2007
Springer
One of the key points in Estimation of Distribution Algorithms (EDAs) is the learning of the probabilistic graphical model used to guide the search: the richer the model the more ...
103
click to vote
UAI
2008
15 years 1 months ago
2008
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
103
click to vote
ICML
2008
IEEE
16 years 12 days ago
2008
IEEE
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
SODA
2001
ACM
15 years 29 days ago
2001
ACM
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
121
click to vote
COLT
2010
Springer
14 years 9 months ago
2010
Springer
We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estim...