• DocumentCode
    324569
  • Title

    Radial basis function classification as computationally efficient kernel regression

  • Author

    Holmström, Lase ; Hoti, Fabian

  • Author_Institution
    Rolf Nevanlinna Inst., Helsinki Univ., Finland
  • Volume
    2
  • fYear
    1998
  • fDate
    4-9 May 1998
  • Firstpage
    1305
  • Abstract
    We consider pattern classification using radial basis function expansions. Such expansions are viewed as computationally efficient forms of kernel regression widely used in statistical literature. The performance of the proposed algorithms are tested in two case studies using speech and handwritten digit data
  • Keywords
    Bayes methods; character recognition; feedforward neural nets; pattern classification; probability; speech recognition; statistical analysis; handwritten digit data; kernel regression; pattern classification; radial basis function classification; radial basis function expansions; speech data; Bayesian methods; Handwriting recognition; Kernel; Pattern recognition; Polynomials; Probability density function; Speech recognition; Statistics; Taxonomy; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-4859-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1998.685963
  • Filename
    685963
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=324569