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.

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting / Andrea Passerini; Paolo Frasconi; Luc De Raedt. - In: JOURNAL OF MACHINE LEARNING RESEARCH. - ISSN 1532-4435. - STAMPA. - 7:(2006), pp. 307-342.

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting

FRASCONI, PAOLO;
2006

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.
7
307
342
Andrea Passerini; Paolo Frasconi; Luc De Raedt
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/386602
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