Fast minimization of structural risk by nearest neighbor rule
Karacali, B.
Krim, H.
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA;
This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: Jan 2003
Volume: 14,
Issue: 1
On page(s): 127- 137
ISSN: 1045-9227
INSPEC Accession Number: 7527259
DOI: 10.1109/TNN.2002.804315
Posted online: 2003-02-06 11:16:36.0
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.
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