Support Vector Machine polyhedral separability in semisupervised learning (Articolo in rivista)

Type
Label
  • Support Vector Machine polyhedral separability in semisupervised learning (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s10957-013-0458-6 (literal)
Alternative label
  • A. Astorino; A. Fuduli (2013)
    Support Vector Machine polyhedral separability in semisupervised learning
    in Journal of optimization theory and applications
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • A. Astorino; A. Fuduli (literal)
Rivista
Note
  • Scopus (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di Calcolo e Reti ad Alte Prestazioni-C.N.R. c/o D.E.I.S., Università della Calabria, 87036 Rende (CS), Italia, e-mail: astorino@icar.cnr.it; Dipartimento di Matematica e Informatica, Università della Calabria, 87036 Rende (CS), Italia, e-mail: antonio.fuduli@unical.it (literal)
Titolo
  • Support Vector Machine polyhedral separability in semisupervised learning (literal)
Abstract
  • We introduce separation margin maximization, a characteristic of the Support Vector Machine technique, into the approach to binary classification based on polyhedral separability and we adopt a semisupervised classification framework. In particular, our model aims at separating two finite and disjoint sets of points by means of a polyhedral surface in the semisupervised case, that is, by exploiting information coming from both labeled and unlabeled samples. Our formulation requires the minimization of a nonconvex nondifferentiable error function. Numerical results are presented on several data sets drawn from the literature. (literal)
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