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ECAI
2010
Springer
13 years 5 months ago
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Dominik Jain, Andreas Barthels, Michael Beetz
COGSCI
2010
87views more  COGSCI 2010»
13 years 6 months ago
The Logical Problem of Language Acquisition: A Probabilistic Perspective
Natural language is full of patterns that appear to fit with general linguistic rules but are ungrammatical. There has been much debate over how children acquire these ‘‘ling...
Anne S. Hsu, Nick Chater
ECML
2006
Springer
13 years 11 months ago
(Agnostic) PAC Learning Concepts in Higher-Order Logic
This paper studies the PAC and agnostic PAC learnability of some standard function classes in the learning in higher-order logic setting introduced by Lloyd et al. In particular, i...
Kee Siong Ng
FGR
2011
IEEE
255views Biometrics» more  FGR 2011»
12 years 11 months ago
Beyond simple features: A large-scale feature search approach to unconstrained face recognition
— Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [1], [2]; HOG [3], [4]; or LBP [5], [6]) that transform raw pixel va...
David D. Cox, Nicolas Pinto
FUIN
2008
108views more  FUIN 2008»
13 years 6 months ago
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
Wannes Meert, Jan Struyf, Hendrik Blockeel