On a Convex Logic Fragment for Learning and Reasoning
By Francesco Giannini, Michelangelo Diligenti, Marco Gori, Marco Maggini. 2018
20 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
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In Artificial Intelligence
- Francesco Giannini
- Michelangelo Diligenti
- Marco Gori
- Giuseppe Marra
- William L. Hamilton
- Jure Leskovec
- Alessio Micheli
- Xavier Bresson
In Computational Theory and Mathematics
Citations in the corpus
- Integrating Learning and Reasoning with Deep Logic Models (2019, background): “In this regard, some recent work shows how to get convex (or even linear) functional constraints exploiting the convex Łukasiewicz fragment [11].”