53 search results - page 4 / 11 » A MINSAT Approach for Learning in Logic Domains |
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
ECAI
2008
Springer
13 years 9 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...
ILP
2007
Springer
14 years 1 months ago
2007
Springer
Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). W...
ICML
2005
IEEE
14 years 8 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...
SEMWEB
2005
Springer
14 years 1 months ago
2005
Springer
Ontology Learning from text aims at generating domain ontologies from textual resources by applying natural language processing and machine learning techniques. It is inherent in t...