• DocumentCode
    1552978
  • Title

    Learning algorithm for nonlinear support vector machines suited for digital VLSI

  • Author

    Anguita, D. ; Boni, A. ; Ridella, S.

  • Author_Institution
    Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
  • Volume
    35
  • Issue
    16
  • fYear
    1999
  • fDate
    8/5/1999 12:00:00 AM
  • Firstpage
    1349
  • Lastpage
    1350
  • Abstract
    A learning algorithm for radial basis function support vector machines (RBF-SVMs) that can be easily implemented in digital VLSI is proposed. It is shown that, as opposed to traditional artificial neural networks, learning in SVMs is very robust with respect to quantisation effects deriving from the finite precision of computations
  • Keywords
    VLSI; digital integrated circuits; learning (artificial intelligence); neural chips; radial basis function networks; artificial neural network; digital VLSI; learning algorithm; nonlinear support vector machine; quantisation; radial basis function network;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:19990950
  • Filename
    790045
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1552978