In this paper, we introduce the convex fragment of Łukasiewicz logic and discuss its possible applications in different learning schemes. The provided theoretical results are highly general because they can be exploited in any learning framework involving logical constraints. The method is of particular interest since the fragment guarantees to deal with convex constraints, which are shown to be equivalent to a set of linear constraints. Within this framework, we are able to formulate learning with kernel machines as well as collective classification as a quadratic programming problem.

On a Convex Logic Fragment for Learning and Reasoning

Giannini, Francesco
;
Gori, Marco;
2019

Abstract

In this paper, we introduce the convex fragment of Łukasiewicz logic and discuss its possible applications in different learning schemes. The provided theoretical results are highly general because they can be exploited in any learning framework involving logical constraints. The method is of particular interest since the fragment guarantees to deal with convex constraints, which are shown to be equivalent to a set of linear constraints. Within this framework, we are able to formulate learning with kernel machines as well as collective classification as a quadratic programming problem.
2019
Settore INFO-01/A - Informatica
Settore IINF-05/A - Sistemi di elaborazione delle informazioni
Collective classification; convex optimization; first-order logicv (FOL); kernel machines; learning from constraints
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/150594
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