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» Bottom-up learning of Markov logic network structure
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IJAR
2010
91views more  IJAR 2010»
14 years 8 months ago
Logical and algorithmic properties of stable conditional independence
The logical and algorithmic properties of stable conditional independence (CI) as an alternative structural representation of conditional independence information are investigated...
Mathias Niepert, Dirk Van Gucht, Marc Gyssens
AMAI
2008
Springer
14 years 9 months ago
Bayesian learning of Bayesian networks with informative priors
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). Prior distributions are defined using stochastic logic programs...
Nicos Angelopoulos, James Cussens
IDEAL
2005
Springer
15 years 3 months ago
Generating Predicate Rules from Neural Networks
Artificial neural networks play an important role for pattern recognition tasks. However, due to poor comprehensibility of the learned network, and the inability to represent expl...
Richi Nayak
TNN
1998
123views more  TNN 1998»
14 years 9 months ago
A general framework for adaptive processing of data structures
—A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to r...
Paolo Frasconi, Marco Gori, Alessandro Sperduti
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FUIN
2008
108views more  FUIN 2008»
14 years 8 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