Abstract
The error probability of the k-nearest neighbor rule converges to the Bayes risk for all distributions when k → ∞, and k/n → 0 as n → ∞. The convergence result is extended here to include data-dependent choices of k. We also look at the data-based selection of a metric and of weights in weighted nearest neighbor rules.
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© 1996 Springer Science+Business Media New York
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Devroye, L., Györfi, L., Lugosi, G. (1996). Automatic Nearest Neighbor Rules. In: A Probabilistic Theory of Pattern Recognition. Stochastic Modelling and Applied Probability, vol 31. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0711-5_26
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DOI: https://doi.org/10.1007/978-1-4612-0711-5_26
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6877-2
Online ISBN: 978-1-4612-0711-5
eBook Packages: Springer Book Archive