Fast learning of relational kernels

Authors:
Niels Landwehr;Andrea Passerini;Luc Raedt;Paolo Frasconi
Affiliations:
Department for Computer Science, University of Potsdam, Potsdam, Germany 14482;Dipartimento di Ingegneria e Scienza dell'Informazione, Università degli Studi di Trento, Povo, Italy 38100;Departement Computerwetenschappen, Katholieke Universiteit Leuven, Heverlee, Belgium 3001;Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Firenze, Italy 50139
Venue:
Machine Learning
Year:
2010

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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.