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Foundations of Computing and Decision Sciences

The Journal of Poznan University of Technology

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Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

M. Kaden / M. Lange / D. Nebel / M. Riedel / T. Geweniger / T. Villmann
  • Corresponding author
  • Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany, www: https://www.mni.hs-mittweida.de/webs/villmann.html
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Published Online: 2014-05-30 | DOI: https://doi.org/10.2478/fcds-2014-0006

Abstract

.Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.

Keywords: learning vector quantization; non-standard metrics; classification; classification certainty; statistics

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About the article

Received: 2014-01-01

Published Online: 2014-05-30

Published in Print: 2014-05-01


Citation Information: Foundations of Computing and Decision Sciences, Volume 39, Issue 2, Pages 79–105, ISSN (Online) 2300-3405, DOI: https://doi.org/10.2478/fcds-2014-0006.

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