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The optimal distance measure for nearest neighbor classification

Short, R., Fukunaga, K.·1981·IEEE Transactions on Information Theory

Abstract

A local distance measure is shown to optimize the performance of the nearest neighbor two-class classifier for a finite number of samples. The difference between the finite sample error and the asymptotic error is used as the criterion of improvement. This new distance measure is compared to the well-known Euclidean distance. An algorithm for practical implementation is introduced. This algorithm is shown to be computationally competitive with the present nearest neighbor procedures and is illustrated experimentally. A closed form for the corresponding second-order moment of this criterion is found. Finally, the above results are extended to

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Metadata

Authors
Short, R., & Fukunaga, K.
Venue
IEEE Transactions on Information Theory
Published
1981-09
Volume
27
Issue
5
Pages
622-627
ISSN
  • 0018-9448 (ISSN-L)
  • 1557-9654
OpenAlex
W2088779313
Cited by
241
References
1
FWCI
2.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The optimal distance measure for nearest neighbor classification. https://app.cochat.ai/papers/10.1109/tit.1981.1056403
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Short, R., & Fukunaga, K. (1981). The optimal distance measure for nearest neighbor classification. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.1981.1056403

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