rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Variable selection for binary classification of spectral data
    sub title: the KDR algorithm
  name:
    name part: Kiffe, Jacqueline M.
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: xx
    date issued: 2001
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xviii, 316 leaves : illustrations ; 28 cm.
  abstract: Classification has been approached almost exclusively as an extension of classical mean separation procedures, which depend upon the law of large numbers for their validity. In classification, however, we are always in the realm of the single observation, and new procedures, which focus on control of the variability of the classification rule, are called for. This paper discusses several alternative classification methods, including a likelihood ratio algorithm that performs as well as traditional Fisherian methods and two additional algorithms (a geometric mean separation maximization algorithm and a distance ratio algorithm) which demonstrate substantially superior robustness to the methods currently in use.
  note: by Jacqueline M. Kiffe.
  note: Vita.
  note: "Major Subject: Statistics".
  note: Issued also on microfiche from University Microfilm Inc.
  note: Ph. D. Texas A&M University 2001.
  note: Includes bibliographical references (leaves 67-78).
  subject:
    topic: Major statistics
  identifier: 30-11743 UMI
  identifier: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  record info:
    record content source: TXA
    record creation date: 020208
    record change date: 20190325102939.0
    record identifier: in00001654641