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Quantum-Hybrid Neural Vector Quantization – A Mathematical Approach

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12854))

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Abstract

The paper demonstrates how to realize neural vector quantizers by means of quantum computing approaches. Particularly, we consider self-organizing maps and the neural gas vector quantizer for unsupervised learning as well as generalized learning vector quantization for classification learning. We show how quantum computing concepts can be adopted for these algorithms. The respective mathematical framework is explained in detail.

A. E. is supported by an ESF PhD grant.

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Acknowledgement

A.E. was supported by a grant of the European Social Fund (ESF).

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Authors and Affiliations

  1. Saxon Institute for Computational Intelligence and Machine Learning (SICIM) at the University of Applied Sciences, Mittweida, Germany

    Thomas Villmann & Alexander Engelsberger

Authors
  1. Thomas Villmann
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  2. Alexander Engelsberger
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Corresponding author

Correspondence to Thomas Villmann .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Villmann, T., Engelsberger, A. (2021). Quantum-Hybrid Neural Vector Quantization – A Mathematical Approach. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_22

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  • DOI: https://doi.org/10.1007/978-3-030-87986-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87985-3

  • Online ISBN: 978-3-030-87986-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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