• DocumentCode
    295955
  • Title

    Training ANN using linear minimax techniques

  • Author

    Charalambous, Chris

  • Author_Institution
    Dept. of Public & Bus. Adm., Cyprus Univ., Nicosia, Cyprus
  • Volume
    1
  • fYear
    1995
  • fDate
    Nov/Dec 1995
  • Firstpage
    84
  • Abstract
    The purpose of this paper is to present a new method for training ANN. The method solves a sequence of linear minimax optimization problems and does not make any assumption of the network structure, but it builds up as the algorithm proceeds. The method does not create unnecessary regions of local minima and it guarantees the classification of the input feature space in a finite number of steps
  • Keywords
    learning (artificial intelligence); minimax techniques; neural nets; pattern classification; classification; input feature space; learning; linear minimax techniques; neural networks; optimization; threshold weight; Linear programming; Minimax techniques; Neurons; Nonlinear equations; Optimization methods; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1995. Proceedings., IEEE International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-2768-3
  • Type

    conf

  • DOI
    10.1109/ICNN.1995.487907
  • Filename
    487907
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=295955