Title: Probabilistic rule learning
Authors: De Raedt, Luc ×
Thon, Ingo #
Issue Date: 2010
Host Document: Lecture Notes in Computer Science, Inductive Logic Programming - 20th International Conference, ILP 2010 vol:6489 pages:47-58
Conference: International Conference on Inductive Logic Programming (ILP) edition:20 location:Florence, Italy date:27-30 June 2010
Abstract: Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a probabilistic setting, in which both the examples themselves as well as their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL, which combines the principles of the relational rule learner FOIL with the probabilistic Prolog, ProbLog. We report also on some experiments that demonstrate the utility of the approach.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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