On the Reduction of the Nearest-Neighbor Variation for More Accurate Classification and Error Estimates
Issue No. 05 - May (1998 vol. 20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.682188
<p><b>Abstract</b>—In designing the <it>nearest-neighbor</it> (<it>NN</it>) 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-<it>NN</it> rule without the rejection option and utilizes a polarization scheme. Simulation results are included as a means of verifying this analysis.</p>
Nearest-neighbor risk, nearest-neighbor classifier, Bayes error, asymptotic risk, risk estimation.
A. Djouadi, "On the Reduction of the Nearest-Neighbor Variation for More Accurate Classification and Error Estimates," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 567-571, 1998.