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6 x 9, illustrated
ISSN
0899-7667
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1530-888X
2014 Impact factor:
2.21

Neural Computation

May 1995, Vol. 7, No. 3, Pages 594-605.
(doi: 10.1162/neco.1995.7.3.594)
© 1995 Massachusetts Institute of Technology
Adaptive Voting Rules for k-Nearest Neighbors Classifiers
Article PDF (584.12 KB)
Abstract

A simple form of cooperation between the k-nearest neighbors (NN) approach to classification and the neural-like property of adaptation is explored. A tunable, high level k-nearest neighbors decision rule is defined that comprehends most previous generalizations of the common majority rule. A learning procedure is developed that applies to this rule and exploits those statistical features that can be induced from the training set. The overall approach is tested on a problem of handwritten character recognition. Experiments show that adaptivity in the decision rule may improve the recognition and rejection capability of standard k-NN classifiers.