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187
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COLT
1994
Springer
15 years 7 months ago
Bayesian Inductive Logic Programming
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learnin...
Stephen Muggleton
130
Voted
KCAP
2003
ACM
15 years 9 months ago
Building large knowledge bases by mass collaboration
Acquiring knowledge has long been the major bottleneck preventing the rapid spread of AI systems. Manual approaches are slow and costly. Machine-learning approaches have limitatio...
Matthew Richardson, Pedro Domingos
133
Voted
CORR
2010
Springer
147views Education» more  CORR 2010»
15 years 3 months ago
Learning Probabilistic Hierarchical Task Networks to Capture User Preferences
While much work on learning in planning focused on learning domain physics (i.e., action models), and search control knowledge, little attention has been paid towards learning use...
Nan Li, William Cushing, Subbarao Kambhampati, Sun...
139
Voted
NIPS
2008
15 years 5 months ago
Deep Learning with Kernel Regularization for Visual Recognition
In this paper we aim to train deep neural networks for rapid visual recognition. The task is highly challenging, largely due to the lack of a meaningful regularizer on the functio...
Kai Yu, Wei Xu, Yihong Gong
147
Voted
PKDD
2009
Springer
170views Data Mining» more  PKDD 2009»
15 years 10 months ago
Statistical Relational Learning with Formal Ontologies
Abstract. We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation...
Achim Rettinger, Matthias Nickles, Volker Tresp