Learning with Nearest Neighbour Classifiers
Neural Processing Letters
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Complete Cross-Validation for Nearest Neighbor Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
IDS false alarm reduction using an instance selection KNN-memetic algorithm
International Journal of Metaheuristics
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The problem of selecting of prototypes is to select a subset in the learning sample for which the set of minimum cardinality would provide the optimum of a given learning quality functional. In this article the problem of classification is considered in two classes, the method of classification by nearest neighbor, and three functional characteristics: the frequency of errors on the entire sample, a cross validation with one separated object, and a complete cross validation with k separated objects. It is shown that the problem of selection of prototypes in all three cases is NP-complete, which justifies the use of well-known heuristic methods for the prototype search.