• DocumentCode
    2702882
  • Title

    Comparing different clustering techniques-RBF networks training

  • Author

    Brizzotti, M.M. ; de Carvalho, A.C.P.L.F.

  • Author_Institution
    Dept. de Ciencias de Comput. e Estatistica, Sao Paulo Univ., Brazil
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    225
  • Lastpage
    230
  • Abstract
    Clustering techniques have a strong influence on the performance achieved by RBF neural networks. The article compares the performance achieved by RBF networks using seven different clustering techniques. For such, different sizes of RBF networks are trained and tested using an automatic target recognition data set. The performances of these RBF networks using each clustering technique are compared and analyzed
  • Keywords
    iterative methods; optimisation; pattern clustering; radial basis function networks; self-organising feature maps; tree searching; unsupervised learning; RBF networks training; automatic target recognition data set; clustering techniques; Automatic testing; Computer networks; Image analysis; Information science; Interpolation; Neural networks; Performance analysis; Radial basis function networks; Target recognition; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
  • Conference_Location
    Rio de Janeiro, RJ
  • ISSN
    1522-4899
  • Print_ISBN
    0-7695-0856-1
  • Type

    conf

  • DOI
    10.1109/SBRN.2000.889743
  • Filename
    889743