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Adaptive Scaling of Codebook Vectors

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Artificial Neural Nets and Genetic Algorithms

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Abstract

In this paper we introduce a vector quantization algorithm in which the codebook vectors are extended with a scale parameter to let them represent Gaussian functions. The means of these functions are determined by a standard vector quantization algorithm; and for their scales we have derived a learning rule. Our algorithm estimates probability densities efficiently. The main application is pattern classification.

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Authors and Affiliations

  1. Department of Computer Science, Utrecht University, P.O.Box 80.089, 3508 TB, Utrecht, The Netherlands

    S. Haring & J. N. Kok

Authors
  1. S. Haring
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  2. J. N. Kok
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© 1995 Springer-Verlag/Wien

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Haring, S., Kok, J.N. (1995). Adaptive Scaling of Codebook Vectors. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_63

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  • DOI: https://doi.org/10.1007/978-3-7091-7535-4_63

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82692-8

  • Online ISBN: 978-3-7091-7535-4

  • eBook Packages: Springer Book Archive

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