• DocumentCode
    303243
  • Title

    A convergence theorem for incremental learning with real-valued inputs

  • Author

    Gordon, Mirta B.

  • Author_Institution
    CEA, Centre d´´Etudes Nucleaires, de Grenoble, France
  • Volume
    1
  • fYear
    1996
  • fDate
    3-6 Jun 1996
  • Firstpage
    381
  • Abstract
    We present a convergence theorem for incremental learning algorithms, valid for real-valued input patterns. The upper bound to the number of hidden units is equal to P-1, where P is the number of patterns in the training set
  • Keywords
    convergence of numerical methods; learning (artificial intelligence); neural nets; pattern classification; set theory; convergence theorem; hidden units; incremental learning; neural networks; parity machine; pattern classification; real-valued inputs; upper bound; Convergence; Machine learning; Neural networks; Neurons; Radiofrequency interference; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1996., IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Print_ISBN
    0-7803-3210-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1996.548922
  • Filename
    548922
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=303243