25 search results - page 5 / 5 » Learning Bounded Treewidth Bayesian Networks |
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
BMCBI
2007
13 years 5 months ago
2007
Background: In vertebrates, a large part of gene transcriptional regulation is operated by cisregulatory modules. These modules are believed to be regulating much of the tissue-sp...
KDD
2002
ACM
14 years 5 months ago
2002
ACM
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
NIPS
1998
13 years 6 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 ...
JAIR
2010
13 years 3 months ago
2010
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...