@article{karacali_krim_2003, title={Fast minimization of structural risk by nearest neighbor rule}, volume={14}, ISSN={["1941-0093"]}, DOI={10.1109/TNN.2002.804315}, abstractNote={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.}, number={1}, journal={IEEE TRANSACTIONS ON NEURAL NETWORKS}, author={Karacali, B and Krim, H}, year={2003}, month={Jan}, pages={127–137} }