ITEM METADATA RECORD
Title: Kernels on Prolog proof trees: Statistical learning in the ILP setting
Authors: Passerini, A ×
Frasconi, P
De Raedt, Luc #
Issue Date: Feb-2006
Publisher: Microtome publishing
Series Title: Journal of machine learning research vol:7 pages:307-342
Abstract: We develop kernels for measuring the similarity between relational instances using background knowledge expressed in first-order logic. The method allows us to bridge the gap between traditional inductive logic programming (ILP) representations and statistical approaches to supervised learning. Logic programs are first used to generate proofs of given visitor programs that use predicates declared in the available background knowledge. A kernel is then defined over pairs of proof trees. The method can be used for supervised learning tasks and is suitable for classification as well as regression. We report positive empirical results on Bongard-like and M-of-N problems that are difficult or impossible to solve with traditional ILP techniques, as well as on real bioinformatics and chemoinformatics data sets.
ISSN: 1532-4435
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Status SizeFormat
jmlr06.pdf Published 395KbAdobe PDFView/Open

 


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science