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Improving Nearest Neighbor Rule with a Simple Adaptive Distance Measure

  • Jigang Wang
  • Predrag Neskovic
  • Leon N. Cooper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. However, it faces serious challenges when patterns of different classes overlap in some regions in the feature space. In the past, many researchers developed various adaptive or discriminant metrics to improve its performance. In this paper, we demonstrate that an extremely simple adaptive distance measure significantly improves the performance of the k-nearest neighbor rule.

Keywords

Distance Measure Class Label Lower Error Rate Manhattan Distance Query Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jigang Wang
    • 1
  • Predrag Neskovic
    • 1
  • Leon N. Cooper
    • 1
  1. 1.Institute for Brain and Neural Systems, Department of PhysicsBrown UniversityProvidenceUSA

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