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Learning Vector Quantization

  • Teuvo Kohonen
Part of the Springer Series in Information Sciences book series (SSINF, volume 30)

Abstract

Closely related to VQ and SOM is Learning Vector Quantization (LVQ). This name signifies a class of related algorithms, such as LVQ1, LVQ2, LVQ3, and OLVQ1. While VQ and the basic SOM are unsupervised clustering and learning methods, LVQ describes supervised learning. On the other hand, unlike in SOM, no neighborhoods around the “winner” are defined during learning in the basic LVQ, whereby also no spatial order of the codebook vectors is expected to ensue.

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References

  1. [6.1]
    T. Kohonen: In Advanced Neural Networks, ed. by R. Eckmiller (Elsevier, Amsterdam, Netherlands 1990) p. 137Google Scholar
  2. [6.2]
    T. Kohonen: In Proc. IJCNN-90-San Diego, Int. Joint Conf. on Neural Networks (IEEE Service Center, Piscataway, NJ 1990) p. I–545Google Scholar
  3. [6.3]
    T. Kohonen: In Theory and Applications of Neural Networks, Proc. First British Neural Network Society Meeting (BNNS, London, UK 1992) p. 235CrossRefGoogle Scholar
  4. [6.4]
    T. Kohonen: In Artificial Neural Networks, ed. by T. Kohonen, K. Mäkisara, O. Simula, J. Kangas (North-Holland, Amsterdam, Netherlands 1991) p. 11–1357Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Teuvo Kohonen
    • 1
  1. 1.Laboratory of Computer and Information ScienceHelsinki University of TechnologyEspoo 15Finland

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