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Achieving the time of -NN, but the accuracy of -NN

Abstract
We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of -NN prediction, while matching (or improving) the faster prediction time of -NN. The approach consists of aggregating denoised -NN predictors over a small number of distributed subsamples. We show, both theoretically and experimentally, that small subsample sizes suffice to attain similar performance as -NN, without sacrificing the computational efficiency of -NN.
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