eprintid: 3131
rev_number: 7
eprint_status: archive
userid: 69
dir: disk0/00/00/31/31
datestamp: 2016-02-26 14:43:14
lastmod: 2016-02-26 14:43:14
status_changed: 2016-02-26 14:43:14
type: conference_item
metadata_visibility: show
creators_name: Sanguineti, Marcello
creators_name: Gnecco, Giorgio
creators_name: Gori, Marco
creators_name: Melacci, Stefano
creators_id: 
creators_id: giorgio.gnecco@imtlucca.it
creators_id: 
creators_id: 
title: Dealing with mixed hard/soft constraints via Support constraint Machines
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
pres_type: paper
abstract: 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
pagerange: 218-219
event_title: 45th Conference of Italian Operational Research Society (AIRO 2015)
event_location: Pisa, Italy
event_dates: September 7-10, 2015
event_type: conference
refereed: TRUE
book_title: Book of abstracts of the 45th Conference of Italian Operational Research Society (AIRO)
official_url: http://www.airo.org/conferences/airo2015/images/booklet.pdf
citation:   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)