275 search results - page 32 / 55 » A geometric view on learning Bayesian network structures |
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
ECAI
2008
Springer
14 years 11 months ago
2008
Springer
Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling com...
ICA
2010
Springer
14 years 10 months ago
2010
Springer
Abstract. We discuss causal structure learning based on linear structural equation models. Conventional learning methods most often assume Gaussianity and create many indistinguish...
NIPS
2008
14 years 11 months ago
2008
Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification in which we predict outputs independently, in ranking we are inter...
JMLR
2002
14 years 9 months ago
2002
In this paper we prove the so-called "Meek Conjecture". In particular, we show that if a DAG H is an independence map of another DAG G, then there exists a finite sequen...