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In this paper, we propose a method that selects a subset of the training data points to update LVQ prototypes. The main goal is to conduct the prototypes to converge at a more convenient location, diminishing misclassification errors. The method selects an update set composed by a subset of points considered to be at the risk of being captured by another class prototype. We associate the proposed methodology to a weighted norm, instead of the Euclidean, in order to establish different levels of relevance for the input attributes. The technique was implemented on a controlled experiment and on Web available data sets.
Index Terms- Learning vector quantization LVQ, pattern classification, clustering, data selection, neural networks.

C. E. Pedreira, "Learning Vector Quantization with Training Data Selection," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 157-162, 2006.
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