• DocumentCode
    288311
  • Title

    Complexity of learning: the case of everyday neural networks

  • Author

    Oláh, B. ; Szepesvári, Cs

  • Author_Institution
    Jozsef Attila Univ., Szeged, Hungary
  • Volume
    1
  • fYear
    1994
  • fDate
    27 Jun-2 Jul 1994
  • Firstpage
    61
  • Abstract
    The authors examine two slightly different domains of the learning problem. They find a polynomial time result, and also an NP-completeness result in these two domains. The domains are chosen so, that commonly used neural network architectures are included
  • Keywords
    computational complexity; learning (artificial intelligence); neural net architecture; neural nets; NP-completeness; complexity; neural networks; polynomial time result; Artificial neural networks; Computer aided software engineering; Computer architecture; Computer networks; Neural networks; Neurons; Polynomials; Supervised learning; Terminology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1901-X
  • Type

    conf

  • DOI
    10.1109/ICNN.1994.374139
  • Filename
    374139
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=288311