Title: Parameter learning in probabilistic databases: A least squares approach
Authors: Gutmann, Bernd ×
Kimmig, Angelika
Kersting, Kristian
De Raedt, Luc #
Issue Date: 17-Aug-2008
Publisher: Springer Verlag
Host Document: Machine Learning and Knowledge Discovery in Databases vol:5211 pages:473-488
Conference: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) location:Antwerp, Belgium date:15-19 September 2008
Article number: 120
Abstract: We introduce the problem of learning the parameters of the probabilistic database ProbLog. Given the observed success probabilities of a set of queries, we compute the probabilities attached to facts that have a low approximation error on the training examples as well as on unseen examples. Assuming Gaussian error terms on the observed success probabilities, this naturally leads to a least squares optimization problem. Our approach, called LeProbLog, is able to learn both from queries and from proofs and even from both simultaneously. This makes it flexible and allows faster training in domains where the proofs are available. Experiments on real world data show the usefulness and effectiveness of this least squares calibration of probabilistic databases.
Description: acceptance rate = 20%
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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ECML_Poster_A4.pdfPoster A4 Published 579KbAdobe PDFView/Open
ecml_talk_gutmann.pdfPresentation slides Published 2276KbAdobe PDFView/Open


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