%T Dealing with mixed hard/soft constraints via Support constraint Machines
%P 218-219
%X A learning paradigm is presented, which extends the classical framework of
learning from examples by including hard pointwise constraints, i.e., constraints that cannot be
violated. In applications, hard pointwise constraints may encode very precise prior knowledge
coming from rules, applied, e.g., to a large collection of unsupervised examples. The classical
learning framework corresponds to soft pointwise constraints, which can be violated at the cost
of some penalization. The functional structure of the optimal solution is derived in terms of a set
of ?support constraints?, which generalize the classical concept of ?support vectors?. They are
at the basis of a novel learning parading, that we called ?Support Constraint Machines?. A case
study and a numerical example are presented.
%D 2015
%L eprints3131
%A Marcello Sanguineti
%A Giorgio Gnecco
%A Marco Gori
%A Stefano Melacci
%C Pisa, Italy
%B Book of abstracts of the 45th Conference of Italian Operational Research Society (AIRO)