Fast minimization of structural risk by nearest neighbor rule
Karacali, B.; Krim, H.
Neural Networks, IEEE Transactions on
Volume 14, Issue 1, Jan 2003 Page(s): 127 - 137
Digital Object Identifier   10.1109/TNN.2002.804315
Summary: In this paper, we present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem. We propose a fast reference set thinning algorithm on the training data set similar to a support vector machine (SVM) approach. We then show that the nearest neighbor rule based on the reduced set implements the structural risk minimization principle, in a manner which does not involve selection of a convenient feature space. Simulation results on real data indicate that this method significantly reduces the computational cost of the conventional SVMs, and achieves a nearly comparable test error performance.

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