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On the Reduction of the Nearest-Neighbor Variation for More Accurate Classification and Error Estimates
May 1998 (vol. 20 no. 5)
pp. 567-571

Abstract—In designing the nearest-neighbor (NN) classifier, a method is presented to produce a finite sample size risk close to the asymptotic one. It is based on an attempt to eliminate the first-order effects of the sample size, as well as all higher odd terms. This method uses the 2-NN rule without the rejection option and utilizes a polarization scheme. Simulation results are included as a means of verifying this analysis.

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Index Terms:
Nearest-neighbor risk, nearest-neighbor classifier, Bayes error, asymptotic risk, risk estimation.
Citation:
Abdelhamid Djouadi, "On the Reduction of the Nearest-Neighbor Variation for More Accurate Classification and Error Estimates," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 5, pp. 567-571, May 1998, doi:10.1109/34.682188
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