248 search results - page 2 / 50 » Learning the Structure of Dynamic Probabilistic Networks |
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
FOSSACS
2005
Springer
14 years 1 months ago
2005
Springer
We study the concept of choice for true concurrency models such as prime event structures and safe Petri nets. We propose a dynamic variation of the notion of cluster previously in...
ICDM
2010
IEEE
13 years 5 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...
ECML
2007
Springer
14 years 1 months ago
2007
Springer
We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm i...