311 search results - page 2 / 63 » Learning Probabilistic Models of Link Structure |
ICMLA
2009
13 years 3 months ago
2009
Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism pro...
ICML
2009
IEEE
14 years 6 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...
ECML
2006
Springer
13 years 9 months ago
2006
Springer
Abstract. We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend na
ECML
2007
Springer
13 years 11 months ago
2007
Springer
Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn...
ICML
2001
IEEE
14 years 6 months ago
2001
IEEE
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data i...