• DocumentCode
    1241578
  • Title

    About Neighborhood Counting Measure Metric and Minimum Risk Metric

  • Author

    Argentini, A. ; Blanzieri, Enrico

  • Author_Institution
    Dipt. di Ing. e Scienza dell´Inf., Univ. di Trento, Trento, Italy
  • Volume
    32
  • Issue
    4
  • fYear
    2010
  • fDate
    4/1/2010 12:00:00 AM
  • Firstpage
    763
  • Lastpage
    765
  • Abstract
    In a 2006 TPAMI paper, Wang proposed the neighborhood counting measure, a similarity measure for the k-NN algorithm. In his paper, Wang mentioned the minimum risk metric (MRM,), an early distance measure based on the minimization of the risk of misclassification. Wang did not compare NCM to MRM because of its allegedly excessive computational load. In this comment paper, we complete the comparison that was missing in Wang´s paper and, from our empirical evaluation, we show that MRM outperforms NCM and that its running time is not prohibitive as Wang suggested.
  • Keywords
    learning (artificial intelligence); pattern classification; early distance measure; k-NN algorithm; k-nearest neighbor algorithm; machine learning; minimum risk metric; neighborhood counting measure metric; pattern recognition; risk minimization; MRM; NCM.; Pattern recognition; distance measures; k-Nearest Neighbors; machine learning;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.2009.69
  • Filename
    4815257