A Classification Method Based on Generalized Eigenvalue Problems (Articolo in rivista)

Type
Label
  • A Classification Method Based on Generalized Eigenvalue Problems (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1080/10556780600883874 (literal)
Alternative label
  • Guarracino Mario Rosario (1), Cifarelli Claudio(2), Seref Onur(3), Pardalos Panos(3) (2007)
    A Classification Method Based on Generalized Eigenvalue Problems
    in Optimization methods & software (Print); TAYLOR & FRANCIS LTD, London (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Guarracino Mario Rosario (1), Cifarelli Claudio(2), Seref Onur(3), Pardalos Panos(3) (literal)
Pagina inizio
  • 73 (literal)
Pagina fine
  • 81 (literal)
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  • Top 5 downloaded paper Y2007, Top 10 cited paper Y2001--Y2009 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.tandfonline.com/doi/pdf/10.1080/10556780600883874 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 22 (literal)
Rivista
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  • 9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Mathematical Reviews on the web (MathSciNet) (literal)
  • SciBase (literal)
  • ACM DL (literal)
  • British Library Inside (literal)
  • Scopus (literal)
  • New Jour (literal)
  • Zetoc (literal)
  • EBSCO (literal)
  • ISI CompuMath Citation Index (literal)
  • Zentralblatt MATH Database (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • (1) High Performance Computing and Networking Institute, National Research Council, Italy (2) Department of Statistic, Probability and Applied Statistics, University of Rome 'La Sapienza', Italy (3) Center for Applied Optimization, University of Florida, Gainesville, FL, 32611-6595, USA (literal)
Titolo
  • A Classification Method Based on Generalized Eigenvalue Problems (literal)
Abstract
  • Binary classification refers to supervised techniques that split a set of points in two classes, with respect to a training set of points whose membership is known for each class. Binary classification plays a central role in the solution of many scientific, financial, engineering, medical and biological problems. Many methods with good classification accuracy are currently available. This work shows how a binary classification problem can be expressed in terms of a generalized eigenvalue problem. A new regularization technique is proposed, which gives results that are comparable to other techniques in use, in terms of classification accuracy. The advantage of this method relies in its lower computational complexity with respect to the existing techniques based on generalized eigenvalue problems. Finally, the method is compared with other methods using benchmark data sets. (literal)
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