Fast learning of relational kernels |
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Authors: | Niels Landwehr Andrea Passerini Luc De Raedt Paolo Frasconi |
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Affiliation: | 1.Department for Computer Science,University of Potsdam,Potsdam,Germany;2.Dipartimento di Ingegneria e Scienza dell’Informazione,Università degli Studi di Trento,Povo,Italy;3.Departement Computerwetenschappen,Katholieke Universiteit Leuven,Heverlee,Belgium;4.Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica,Università degli Studi di Firenze,Firenze,Italy |
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Abstract: | We develop a general theoretical framework for statistical logical learning with kernels based on dynamic propositionalization,
where structure learning corresponds to inferring a suitable kernel on logical objects, and parameter learning corresponds
to function learning in the resulting reproducing kernel Hilbert space. In particular, we study the case where structure learning
is performed by a simple FOIL-like algorithm, and propose alternative scoring functions for guiding the search process. We
present an empirical evaluation on several data sets in the single-task as well as in the multi-task setting. |
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