Assessing the reliability of complex networks: empirical models based on machine learning (Contributo in volume (capitolo o saggio))

Type
Label
  • Assessing the reliability of complex networks: empirical models based on machine learning (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1142/9789812774118_0040 (literal)
Alternative label
  • C. M. Rocco, M. Muselli (2006)
    Assessing the reliability of complex networks: empirical models based on machine learning
    World Scientific Publ. Co. Pte. Ltd., Singapore (Singapore) in Applied Artificial Intelligence, 2006
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • C. M. Rocco, M. Muselli (literal)
Pagina inizio
  • 267 (literal)
Pagina fine
  • 274 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Applied Artificial Intelligence (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • C. M. Rocco: Universidad Central de Venezuela, Facultad de Ingeniería, Caracas, Venezuela; M. Muselli: CNR-IEIIT, Genova, Italy. (literal)
Titolo
  • Assessing the reliability of complex networks: empirical models based on machine learning (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 981-256-690-2 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • D. Ruan, P. D'Hondt, P. F. Fantoni, M. De Cock, M. Nachtegael, E. E. Kerre (literal)
Abstract
  • In this paper three models derived using Machine Learning techniques (Support Vector Machines, Decision Trees and Shadow Clustering) are compared for approximating the reliability of real complex networks, such as for water supply, electric power or gas distribution systems or telephone systems, using different reliability criteria. (literal)
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