Consistency of Empirical Risk Minimization for Unbounded Loss Functions (Contributo in volume (capitolo o saggio))

Type
Label
  • Consistency of Empirical Risk Minimization for Unbounded Loss Functions (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/1-4020-3432-6_31 (literal)
Alternative label
  • M. Muselli, F. Ruffino (2005)
    Consistency of Empirical Risk Minimization for Unbounded Loss Functions
    Springer-Verlag, Berlin (Germania) in Biological and Artificial Intelligence Environments, 2005
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • M. Muselli, F. Ruffino (literal)
Pagina inizio
  • 261 (literal)
Pagina fine
  • 270 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#citta
  • Berlino (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Biological and Artificial Intelligence Environments (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • M. Muselli, F. Ruffino: CNR-IEIIT, Genova, Italy (literal)
Titolo
  • Consistency of Empirical Risk Minimization for Unbounded Loss Functions (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
  • Biological and Artificial Intelligence Environments (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 1-4020-3431-8 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • B. Apolloni, M. Marinaro, R. Tagliaferri (literal)
Abstract
  • The theoretical framework of Statistical Learning Theory (SLT) for pattern recognition problems is extended to comprehend the situations where an infinite value of the loss function is employed to prevent misclassifications in specific regions with high reliability. Sufficient conditions for ensuring the consistency of the Empirical Risk Minimization (ERM) criterion are then established and an explicit bound, in terms of the VC dimension of the class of decision functions employed to solve the problem, is derived. (literal)
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