TY  - CONF
N2  - 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.
ID  - eprints3135
Y1  - 2014///
AV  - none
M2  - Barcelona, Spain
UR  - http://www.ifors2014.org/files2/program-ifors2014.pdf
A1  - Gnecco, Giorgio
A1  - Gori, Marco
A1  - Melacci, Stefano
A1  - Sanguineti, Marcello
T2  - 20th Conference of the International Federation of Operational Research Societies (IFORS 2014)
TI  - A Machine-Learning Paradigm that Includes Pointwise
Constraints
ER  -