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» Learning Structurally Indeterminate Clauses
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ILP
1998
Springer
13 years 9 months ago
Learning Structurally Indeterminate Clauses
This paper describes a new kind of language bias, S-structural indeterminate clauses, which takes into account the meaning of predicates that play a key role in the complexity of l...
Jean-Daniel Zucker, Jean-Gabriel Ganascia
CP
2007
Springer
13 years 11 months ago
Limitations of Restricted Branching in Clause Learning
The techniques for making decisions, i.e., branching, play a central role in complete methods for solving structured CSP instances. In practice, there are cases when SAT solvers be...
Matti Järvisalo, Tommi A. Junttila
ICML
2009
IEEE
14 years 6 months ago
Learning Markov logic network structure via hypergraph lifting
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. Learning ML...
Stanley Kok, Pedro Domingos
ILP
2007
Springer
13 years 11 months ago
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates
Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). W...
Mark Goadrich, Jude W. Shavlik
ICML
2010
IEEE
13 years 6 months ago
Learning Markov Logic Networks Using Structural Motifs
Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extre...
Stanley Kok, Pedro Domingos