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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© 1997 Springer-Verlag Berlin Heidelberg
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Kohonen, T. (1997). Learning Vector Quantization. In: Self-Organizing Maps. Springer Series in Information Sciences, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-97966-8_6
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DOI: https://doi.org/10.1007/978-3-642-97966-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62017-4
Online ISBN: 978-3-642-97966-8
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