title: Fast minimization of structural risk by nearest neighbor rule.
abstract: 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.
authors.affiliation: Dept. of Electr. and Comput. Eng., North Carolina State Univ., Raleigh, NC, USA.
authors.forename: B
authors.initials: B
authors.lastname: Karacali
authors.forename: H
authors.initials: H
authors.lastname: Krim