• DocumentCode
    1271036
  • Title

    Class-specific feature sets in classification

  • Author

    Baggenstoss, Paul M.

  • Author_Institution
    Naval Underwater Syst. Center, Newport, RI, USA
  • Volume
    47
  • Issue
    12
  • fYear
    1999
  • fDate
    12/1/1999 12:00:00 AM
  • Firstpage
    3428
  • Lastpage
    3432
  • Abstract
    In this correspondence, we present a new approach to the design of probabilistic classifiers that circumvents the dimensionality problem. Rather than working with a common high-dimensional feature set, the classifier is written in terms of likelihood ratios with respect to a common class using sufficient statistics chosen specifically for each class
  • Keywords
    probability; signal classification; statistical analysis; Class-specific feature sets; dimensionality problem; likelihood ratios; probabilistic classifiers; signal classification; sufficient statistics; Bayesian methods; Signal analysis; State estimation; Statistics; Training data; White noise;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.806092
  • Filename
    806092
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1271036