Switching neural networks: A new connectionist model for classification (Articolo in rivista)

Type
Label
  • Switching neural networks: A new connectionist model for classification (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/11731177_4 (literal)
Alternative label
  • M. Muselli (2006)
    Switching neural networks: A new connectionist model for classification
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • M. Muselli (literal)
Pagina inizio
  • 23 (literal)
Pagina fine
  • 30 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 3931 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • M. Muselli: CNR-IEIIT, Genova, Italy (literal)
Titolo
  • Switching neural networks: A new connectionist model for classification (literal)
Abstract
  • A new connectionist model, called Switching Neural Network (SNN), for the solution of classification problems is presented. SNN in- cludes a first layer containing a particular kind of A/D converters, called latticizers, that suitably transform input vectors into binary strings. Then, the subsequent two layers of an SNN realize a positive Boolean function that solve in a lattice domain the original classi¯cation problem. Every function realized by an SNN can be written in terms of intelligi- ble rules. Training can be performed by adopting a proper method for positive Boolean function reconstruction, called Shadow Clustering (SC). Simulation results obtained on the StatLog benchmark show the good quality of the SNNs trained with SC. (literal)
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