A modification of the LAESA algorithm for approximated k-NN classification

نویسندگان

  • Francisco Moreno-Seco
  • Luisa Micó
  • José Oncina
چکیده

Nearest-neighbour (NN) and k-nearest-neighbours (k-NN) techniques are widely used in many pattern recognition classification tasks. The linear approximating and eliminating search algorithm (LAESA) is a fast NN algorithm which does not assume that the prototypes are defined in a vector space; it only makes use of some of the distance properties (mainly the triangle inequality) in order to avoid distance computations. In this work we propose an improvement of LAESA that uses k neighbours in order to approach to the accuracy of a k-NN classifier, and computes the same number of distances than the LAESA preserving the time and space complexity independent from k. 2002 Elsevier Science B.V. All rights reserved.

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عنوان ژورنال:
  • Pattern Recognition Letters

دوره 24  شماره 

صفحات  -

تاریخ انتشار 2003