An upper bound on prototype set size for condensed nearest neighbor

Eric Christiansen

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arXiv · 1309.7676

An upper bound on prototype set size for condensed nearest neighbor

Eric Christiansen

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Abstract

The condensed nearest neighbor (CNN) algorithm is a heuristic for reducing the number of prototypical points stored by a nearest neighbor classifier, while keeping the classification rule given by the reduced prototypical set consistent with the full set. I present an upper bound on the number of prototypical points accumulated by CNN. The bound originates in a bound on the number of times the decision rule is updated during training in the multiclass perceptron algorithm, and thus is independent of training set size.

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Eric Christiansen. 2013-09-29. An upper bound on prototype set size for condensed nearest neighbor. https://arxiv.org/abs/1309.7676

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