• DocumentCode
    2346169
  • Title

    The finite-sample risk of the k-nearest-neighbor classifier under the Lp metric

  • Author

    Snapp, Robert R. ; Venkatesh, Santosh S.

  • Author_Institution
    Dept. of Comput. Sci. & Electr. Eng., Vermont Univ., Burlington, VT, USA
  • fYear
    1994
  • fDate
    27-29 Oct 1994
  • Firstpage
    98
  • Abstract
    The finite-sample risk of the k-nearest neighbor classifier that uses an L2 distance function is examined. For a family of classification problems with smooth distributions in Rn, the risk can be represented as an asymptotic expansion in inverse powers of the n-th root of the reference-sample size. The leading coefficients of this expansion suggest that the Euclidean or L2 distance function minimizes the risk for sufficiently large reference samples
  • Keywords
    pattern classification; random processes; signal sampling; smoothing methods; Euclidean distance function; Lp metric; asymptotic expansion; classification problems; distance function; finite-sample risk; inverse powers; k-nearest-neighbor classifier; leading coefficients; pattern classification; random sample; reference-sample size; smooth distributions; Computer science; Euclidean distance; Laboratories; Nearest neighbor searches; Random processes; Testing; USA Councils;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
  • Conference_Location
    Alexandria, VA
  • Print_ISBN
    0-7803-2761-6
  • Type

    conf

  • DOI
    10.1109/WITS.1994.513925
  • Filename
    513925
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2346169