TY  - CONF
EP  - 219
T2  - 45th Conference of Italian Operational Research Society (AIRO 2015)
ID  - eprints3131
UR  - http://www.airo.org/conferences/airo2015/images/booklet.pdf
Y1  - 2015///
TI  - Dealing with mixed hard/soft constraints via Support constraint Machines
AV  - none
N2  - 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.
SP  - 218
M2  - Pisa, Italy
A1  - Sanguineti, Marcello
A1  - Gnecco, Giorgio
A1  - Gori, Marco
A1  - Melacci, Stefano
ER  -