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An inductive logic programming approach to statistical relational learning.


An inductive logic programming approach to statistical relational learning.

Kersting, Kristian.

IOS Press


228 pages



Frontiers in artificial intelligence and applications; v.148; Dissertations in artificial intelligence


In artificial intelligence, statistical relational learning addresses the integration of probabilistic reasoning with first order logic representation and machine learning. In this treatise, Kersting (Institute for Computer Science, Albert-Ludwigs-U. Freiburg, Germany) develops a general framework of probabilistic inductive logic programming as a foundation for his approach to statistical relational learning, which incorporates the logical concepts of objects and relations among objects into Bayesian networks. Further, Bayesian networks are upgraded to Bayesian logic programs, hidden Markov models to logical hidden Markov models, and Markov decision process to Markov decision programs. Finally, he seeks to show that statistical relational learning approaches naturally yield kernels for structured data and demonstrates these approaches using examples from genetics, bio-informatics, and classical planning domains.

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Publication:SciTech Book News
Article Type:Book Review
Date:Sep 1, 2007
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