Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Outlier Detection Using Classifier Instability
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Decontamination of Training Samples for Supervised Pattern Recognition Methods
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Ongoing Learning for Supervised Pattern Recognition
SIBGRAPI '01 Proceedings of the XIV Brazilian Symposium on Computer Graphics and Image Processing
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This paper presents some ideas about automatic procedures to implement a system with the capability of detecting patterns arising from classes not represented in the training sample. The procedure aims at incorporating automatically to the training sample the necessary information about the new class for correctly recognizing patterns from this class in future classification tasks. The Nearest Neighbor rule is employed as the central classifier and several techniques are added to cope with the peril of incorporating noisy data to the training sample. Experimental results with real data confirm the benefits of the proposed procedure.