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ICML
2010
IEEE
13 years 11 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
VL
2010
IEEE
216views Visual Languages» more  VL 2010»
13 years 8 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...

Book
796views
15 years 9 months ago
Introduction to Machine Learning
This is an introductory book about machine learning. Notice that this is a draft book. It may contain typos, mistakes, etc. The book covers the following topics: Boolean Functio...
Nils J. Nilsson
ML
2010
ACM
175views Machine Learning» more  ML 2010»
13 years 5 months ago
Concept learning in description logics using refinement operators
With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applica...
Jens Lehmann, Pascal Hitzler
ICML
2009
IEEE
14 years 11 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