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» A MINSAT Approach for Learning in Logic Domains
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ICML
2009
IEEE
14 years 7 months ago
Deep transfer via second-order Markov logic
Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, tes...
Jesse Davis, Pedro Domingos
OTM
2004
Springer
14 years 7 days ago
Domain Ontology as a Resource Providing Adaptivity in eLearning
Abstract. This paper presents a knowledge-based approach to eLearning, where the domain ontology plays central role as a resource structuring the learning content and supporting ...
Galia Angelova, Ognian Kalaydjiev, Albena Strupcha...
ILP
2007
Springer
14 years 1 months ago
Learning to Assign Degrees of Belief in Relational Domains
A recurrent question in the design of intelligent agents is how to assign degrees of beliefs, or subjective probabilities, to various events in a relational environment. In the sta...
Frédéric Koriche
AIIA
2005
Springer
14 years 13 days ago
Handling Continuous-Valued Attributes in Incremental First-Order Rules Learning
Machine Learning systems are often distinguished according to the kind of representation they use, which can be either propositional or first-order logic. The framework working wi...
Teresa Maria Altomare Basile, Floriana Esposito, N...
ML
2006
ACM
131views Machine Learning» more  ML 2006»
13 years 6 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos