• DocumentCode
    3652770
  • Title

    Minimal distance neural methods

  • Author

    W. Dich;K. Grudzinski;G.H.F. Diercksen

  • Author_Institution
    Dept. of Comput. Methods, Nicholas Copernicus Univ., Torun, Poland
  • Volume
    2
  • fYear
    1998
  • Firstpage
    1299
  • Abstract
    A general framework for minimal distance methods is presented. Radial basis functions (RBFs) and multilayer perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on real-world datasets obtaining very encouraging results.
  • Keywords
    "Nearest neighbor searches","Testing","Learning","Astrophysics","Multilayer perceptrons","Neural networks","Multi-layer neural network","Large-scale systems","Classification algorithms","Pattern recognition"
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-4859-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1998.685962
  • Filename
    685962
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3652770