• DocumentCode
    993960
  • Title

    Vector quantization technique for nonparametric classifier design

  • Author

    Xie, Qiaobing ; Laszlo, Charles A. ; Ward, Rabab K.

  • Author_Institution
    Dept. of Electr. Eng., British Columbia Univ., Vancouver, BC, Canada
  • Volume
    15
  • Issue
    12
  • fYear
    1993
  • fDate
    12/1/1993 12:00:00 AM
  • Firstpage
    1326
  • Lastpage
    1330
  • Abstract
    An effective data reduction technique based on vector quantization is introduced for nonparametric classifier design. Two new nonparametric classifiers are developed, and their performance is evaluated using various examples. The new methods maintain a classification accuracy that is competitive with that of classical methods but, at the same time, yields very high data reduction rates
  • Keywords
    approximation theory; data reduction; pattern recognition; vector quantisation; Parzen kernel classifier; condensing algorithm; data reduction rates; design; k-nearest neighbour; nonparametric classifier; vector quantization; Application software; Color; Image analysis; Kernel; Multispectral imaging; Optimized production technology; Pattern analysis; Shape; Spatial resolution; Vector quantization;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.250849
  • Filename
    250849
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=993960