Towards Combining Inductive Logic Programming with Bayesian Networks
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Recently, new representation languages that integrate first order logic with Bayesian networks have been developed. Bayesian logic programs are one of these languages. In this paper, we present results on combining Inductive Logic Programming (ILP) with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs. More precisely, we show how to combine the ILP setting learning from interpretations with score-based techniques for learning Bayesian networks. Thus, the paper positively answers Koller and Pfeffer’s question, whether techniques from ILP could help to learn the logical component of first order probabilistic models.
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- Towards Combining Inductive Logic Programming with Bayesian Networks
- Book Title
- Inductive Logic Programming
- Book Subtitle
- 11th International Conference, ILP 2001 Strasbourg, France, September 9–11, 2001 Proceedings
- pp 118-131
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Université Paris Sud, LRI
- 2. Ecole Polytechnique, LMS
- Author Affiliations
- 5. Institute for Computer Science, Machine Learning Lab, Albert-Ludwigs-University, Georges-Köhler-Allee, Gebäude 079, D-79085, Freiburg i. Brg., Germany
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