A Novel Approach to Regularising 1NN classifier for Improved Generalization

In this paper, we propose a class of non-parametric classifiers, that learn arbitrary boundaries and generalize well.
Our approach is based on a novel way to regularize 1NN classifiers using a greedy approach. We refer to this class of classifiers as Watershed Classifiers. 1NN classifiers are known to trivially over-fit but have very large VC dimension, hence do not generalize well. We show that watershed classifiers can find arbitrary boundaries on any dense enough dataset, and, at the same time, …

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