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Machine Learning and Applications, Fourth International Conference on (2009)
Miami Beach, Florida
Dec. 13, 2009 to Dec. 15, 2009
ISBN: 978-0-7695-3926-3
pp: 141-146
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally quantified conditional influence statements that capture local interactions between object attributes. The effects of different conditional influence statements can be combined using rules such as {\sf Noisy-OR}. To combine multiple instantiations of the same rule we need other combining rules at a lower level. In this paper we derive and implement algorithms based on gradient-descent and EM for learning the parameters of these multi-level combining rules. We compare our approaches to learning in Markov Logic Networks and show superior performance in multiple domains.
Statistical Relational Learning, Graphical Models

J. Shavlik, S. Natarajan, G. Kunapuli and P. Tadepalli, "Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule," Machine Learning and Applications, Fourth International Conference on(ICMLA), Miami Beach, Florida, 2009, pp. 141-146.
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