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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 ... (Update)
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BibTeX entry: (Update)
@misc{ passerini-kernels,
author = "Andrea Passerini and Paolo Frasconi and Luc De Raedt",
title = "Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting",
url = "citeseer.ist.psu.edu/passerini06kernels.html" }
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