Identifying Clusters Using Growing Neural Gas: First Results (Contributo in atti di convegno)

Type
Label
  • Identifying Clusters Using Growing Neural Gas: First Results (Contributo in atti di convegno) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-04274-4_56 (literal)
Alternative label
  • Rizzo Riccardo, Urso Alfonso (2009)
    Identifying Clusters Using Growing Neural Gas: First Results
    in International Conference on Artificial Neural Networks, ICANN 2009, Liamassol
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Rizzo Riccardo, Urso Alfonso (literal)
Pagina inizio
  • 536 (literal)
Pagina fine
  • 545 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Artificial Neural Networks - ICANN 2009 - 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part I (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 5768 (literal)
Note
  • Google Scholar (literal)
  • Scopus (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR-CNR (literal)
Titolo
  • Identifying Clusters Using Growing Neural Gas: First Results (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-04273-7 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Cesare Alippi, Marios Polycarpou, Christos Panayiotou, Georgios Ellinas (literal)
Abstract
  • Growing Neural Gas is a self organizing network capable to build a lattice of neural unit that grows in the input pattern manifold. The structure of the obtained network often is not a planar graph and can be not suitable for visualization; cluster identification is possible only if a set of not connected subgraphs are produced. In this work we propose a method to select the neural units in order to extract the information on the pattern clusters, even if the obtained network graph is connected. The proposed method creates a new structure called Labeling Network (LNet) that repeats the topology of the GNG network and a set of weights to the links of the neuron graph. These weights are trained using an anti-Hebbian algorithm obtaining a new structure capable to label input patterns according to their cluster. (literal)
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