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ILP
2007
Springer
14 years 11 days 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
CORR
2010
Springer
136views Education» more  CORR 2010»
13 years 6 months ago
On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have...
Angelika Kimmig, Bart Demoen, Luc De Raedt, V&iacu...
CSL
2002
Springer
13 years 6 months ago
Learning visually grounded words and syntax for a scene description task
A spoken language generation system has been developed that learns to describe objects in computer-generated visual scenes. The system is trained by a `show-and-tell' procedu...
Deb K. Roy
JMLR
2010
111views more  JMLR 2010»
13 years 1 months ago
An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models
Logic-based probabilistic models (LBPMs) enable us to handle problems with uncertainty succinctly thanks to the expressive power of logic. However, most of LBPMs have restrictions...
Masakazu Ishihata, Yoshitaka Kameya, Taisuke Sato,...
DEXA
2008
Springer
123views Database» more  DEXA 2008»
13 years 8 months ago
Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies
Abstract. We present a method based on clustering techniques to detect concept drift or novelty in a knowledge base expressed in Description Logics. The method exploits an effectiv...
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito