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The 2-NN Rule for More Accurate NN Risk Estimation

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2 Author(s)
Fukunaga, Keinosuke ; Department of Electrical Engineering, Purdue University, West Lafayette, IN 47907. ; Flick, Thomas E.

By proper design of a nearest-neighbor (NN) rule, it is possible to reduce effects of sample size in NN risk estimation. The 2-NN rule for the two-class problem eliminates the first-order effects of sample size. Since its asymptotic value is exactly half that of the 1-NN rule, it is possible to substitute the 2-NN rule for the 1-NN rule with a resultant increase in accuracy. For further stabilization of the risk estimate with respect to sample size, 2-NN polarization is suggested. Examples are included. The 2-NN approach is extended to M-class and 2k-NN.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-7 ,  Issue: 1 )