A machine learning algorithm to estimate minimal cut and path sets from a Monte Carlo simulation (Contributo in atti di convegno)

Type
Label
  • A machine learning algorithm to estimate minimal cut and path sets from a Monte Carlo simulation (Contributo in atti di convegno) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Alternative label
  • C. M. Rocco, M. Muselli (2004)
    A machine learning algorithm to estimate minimal cut and path sets from a Monte Carlo simulation
    in 7th International Conference on Probabilistic Safety Assessment and Management, Berlin, Germany, 14-18 June 2004
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • C. M. Rocco, M. Muselli (literal)
Pagina inizio
  • 3142 (literal)
Pagina fine
  • 3147 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Probabilistic Safety Assessment and Management (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • C. M. Rocco: Facultad de Ingeniería, Universidad Central, Caracas, Venezuela M. Muselli: CNR-IEIIT, Genova, Italy (literal)
Titolo
  • A machine learning algorithm to estimate minimal cut and path sets from a Monte Carlo simulation (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
  • Probabilistic Safety Assessment and Management (PSAM7–ESREL ’04) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 1-85233-827-X (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • C. Spitzer, U. Schmocker, V.N. Dang (literal)
Abstract
  • In this paper a novel approach based on a machine learning algorithm (Hamming Clustering) is proposed to estimate the minimal cut and path sets, using the samples generated by a Monte Carlo simulation and any Evaluation Function. Two examples show the potential of the proposed approach. (literal)
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