305 search results - page 34 / 61 » Bayesian Learning of Markov Network Structure |
ICPR
2002
IEEE
14 years 8 months ago
2002
IEEE
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
GECCO
2004
Springer
14 years 1 months ago
2004
Springer
Abstract. Factored Markov Decision Processes is the theoretical framework underlying multi-step Learning Classifier Systems research. This framework is mostly used in the context ...
JMLR
2008
13 years 7 months ago
2008
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
UAI
2004
13 years 9 months ago
2004
Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property ...
CORR
2010
Springer
13 years 6 months ago
2010
Springer
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3 N) is an effective approach. All state-of-...