%0 Conference Paper
%A Gnecco, Giorgio
%A Gori, Marco
%A Melacci, Stefano
%A Sanguineti, Marcello
%B 20th Conference of the International Federation of Operational Research Societies (IFORS 2014)
%C Barcelona, Spain
%D 2014
%F eprints:3135
%T A Machine-Learning Paradigm that Includes Pointwise  Constraints
%U http://eprints.imtlucca.it/3135/
%X 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.