Learning vector quantization with training data selection.

Carlos E Pedreira

doi:10.1109/tpami.2006.14

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Learning vector quantization with training data selection.

Carlos E Pedreira

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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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Algorithms Artificial Intelligence Computer Simulation Computing Methodologies Models, Theoretical Pattern Recognition, Automated Systems Theory Journal Article Research Support, Non-U.S. Gov't

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Carlos E Pedreira. 2006. Learning vector quantization with training data selection.. https://doi.org/10.1109/tpami.2006.14

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