65 search results - page 4 / 13 » On Local Optima in Learning Bayesian Networks |
ICML
2010
IEEE
13 years 9 months ago
2010
IEEE
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
ML
2006
ACM
13 years 8 months ago
2006
ACM
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
ICDM
2010
IEEE
13 years 6 months ago
2010
IEEE
Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
IJAR
2007
13 years 8 months ago
2007
The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. These restrictions may codify expert knowledge in a given domai...
ECSQARU
2005
Springer
14 years 2 months ago
2005
Springer
In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a g...