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A Machine-Learning Paradigm that Includes Pointwise Constraints

Gnecco, Giorgio and Gori, Marco and Melacci, Stefano and Sanguineti, Marcello A Machine-Learning Paradigm that Includes Pointwise Constraints. In: 20th Conference of the International Federation of Operational Research Societies (IFORS 2014), July 13-18, 2014, Barcelona, Spain (2014)

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The classical framework of learning from examples is enhanced by the introduction of hard point-wise constraints, i.e., constraints, on a finite set of examples, that cannot be violated. They arise, e.g., when imposing coherent decisions of classifiers acting on different views of the same pattern. Constrained variational calculus is exploited to derive a representer theorem that provides a description of the functional structure of the solution. The general theory is applied to learning from hard linear point-wise constraints combined with classical supervised pairs and loss functions.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 26 Feb 2016 15:16
Last Modified: 26 Feb 2016 15:16
URI: http://eprints.imtlucca.it/id/eprint/3135

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