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Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection

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Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9257))

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Abstract

We consider a reject option for prototype-based Learning Vector Quantization (LVQ), which facilitates the detection of outliers in the data during the classification process. The rejection mechanism is based on a distance-based criterion and the corresponding threshold is automatically adjusted in the training phase according to pre-defined rejection costs. The adaptation of LVQ prototypes is simultaneously guided by the complementary aims of low classification error, faithful representation of the observed data, and low total rejection costs.

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Author information

Authors and Affiliations

  1. Computational Intelligence Group, University of Applied Sciences Mittweida, Mittweida, Germany

    Thomas Villmann, Marika Kaden & David Nebel

  2. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, P.O. Box 407, 9700 AK, Groningen, The Netherlands

    Michael Biehl

Authors
  1. Thomas Villmann
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  2. Marika Kaden
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  3. David Nebel
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  4. Michael Biehl
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Corresponding author

Correspondence to Thomas Villmann .

Editor information

Editors and Affiliations

  1. University of Malta, Msida, Malta

    George Azzopardi

  2. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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© 2015 Springer International Publishing Switzerland

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Villmann, T., Kaden, M., Nebel, D., Biehl, M. (2015). Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_66

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  • DOI: https://doi.org/10.1007/978-3-319-23117-4_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23116-7

  • Online ISBN: 978-3-319-23117-4

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