relation: http://eprints.imtlucca.it/3131/
title: Dealing with mixed hard/soft constraints via Support constraint Machines
creator: Sanguineti, Marcello
creator: Gnecco, Giorgio
creator: Gori, Marco
creator: Melacci, Stefano
subject: QA75 Electronic computers. Computer science
description: 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.
date: 2015
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Sanguineti, Marcello and Gnecco, Giorgio and Gori, Marco and Melacci, Stefano  Dealing with mixed hard/soft constraints via Support constraint Machines.  In: 45th Conference of Italian Operational Research Society (AIRO 2015), September 7-10, 2015, Pisa, Italy pp. 218-219.        (2015)  
relation: http://www.airo.org/conferences/airo2015/images/booklet.pdf