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A Batch Learning Vector Quantization Algorithm for Nearest Neighbour Classification

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Abstract

We introduce a batch learning algorithm to design the set of prototypes of 1 nearest-neighbour classifiers. Like Kohonen's LVQ algorithms, this procedure tends to perform vector quantization over a probability density function that has zero points at Bayes borders. Although it differs significantly from their online counterparts since: (1) its statistical goal is clearer and better defined; and (2) it converges superlinearly due to its use of the very fast Newton's optimization method. Experiments results using artificial data confirm faster training time and better classification performance than Kohonen's LVQ algorithms.

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Authors and Affiliations

  1. Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC), Gran Capità s/n, C4 building, 08034, Barcelona, Spain

    Sergio Bermejo

  2. Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC), Gran Capità s/n, C4 building, 08034, Barcelona, Spain

    Joan Cabestany

Authors
  1. Sergio Bermejo
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  2. Joan Cabestany
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Bermejo, S., Cabestany, J. A Batch Learning Vector Quantization Algorithm for Nearest Neighbour Classification. Neural Processing Letters 11, 173–184 (2000). https://doi.org/10.1023/A:1009634824627

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  • DOI: https://doi.org/10.1023/A:1009634824627

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