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arXiv · 1809.06778

On a Convex Logic Fragment for Learning and Reasoning

Francesco Giannini·Michelangelo Diligenti·Marco Gori·Marco Maggini
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Abstract

In this paper we introduce the convex fragment of {\L}ukasiewicz Logic and discuss its possible applications in different learning schemes. Indeed, 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.

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Francesco Giannini, Michelangelo Diligenti, Marco Gori, Marco Maggini. 2018-09-18. On a Convex Logic Fragment for Learning and Reasoning. https://doi.org/10.1109/tfuzz.2018.2879627

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