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  • Title: Learning vector quantization with training data selection.
    Author: Pedreira CE.
    Journal: IEEE Trans Pattern Anal Mach Intell; 2006 Jan; 28(1):157-62. PubMed ID: 16402629.
    Abstract:
    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.
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