eprintid: 3135
rev_number: 6
eprint_status: archive
userid: 69
dir: disk0/00/00/31/35
datestamp: 2016-02-26 15:16:19
lastmod: 2016-02-26 15:16:19
status_changed: 2016-02-26 15:16:19
type: conference_item
metadata_visibility: show
creators_name: Gnecco, Giorgio
creators_name: Gori, Marco
creators_name: Melacci, Stefano
creators_name: Sanguineti, Marcello
creators_id: giorgio.gnecco@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
title: A Machine-Learning Paradigm that Includes Pointwise
Constraints
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
pres_type: paper
abstract: 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.
date: 2014
date_type: published
event_title: 20th Conference of the International Federation of Operational Research Societies (IFORS 2014)
event_location: Barcelona, Spain
event_dates: July 13-18, 2014
event_type: conference
refereed: TRUE
book_title: Book of abstracts of the 20th Conference of the International Federation of Operational Research Societies (IFORS 2014)
official_url: http://www.ifors2014.org/files2/program-ifors2014.pdf
citation:   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)