We present two online gradient learning algorithms to design condensed k-nearest neighbor (NN) classifiers. The goal of these learning procedures is to minimize a measure of performance closely related to the expected misclassification rate of the k-NN classifier. One possible implementation of the algorithm is given. Converge properties are analyzed and connections with other works are established. We compare these learning procedures with Kononen's LVQ algorithms [7] and k-NN classification using the handwritten NIST databases [5]. Experimental results demonstrate the potential of the proposed learning algorithms.
CITATION STYLE
Bermejo, S., & Cabestany, J. (1999). On-line gradient learning algorithms for K-nearest neighbor classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1606, pp. 546–555). Springer Verlag. https://doi.org/10.1007/BFb0098212
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