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Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning

A Mathematical Characterization

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9693))

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Abstract

Data dissimilarities and similarities are the key ingredients of machine learning. We give a mathematical characterization and classification of those measures based on structural properties also involving psychological-cognitive aspects of similarity determination, and investigate admissible conversions. Finally, we discuss some consequences of the obtained taxonomy and their implications for machine learning algorithms.

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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. Staatliche Berufliche Oberschule Kaufbeuren, Kaufbeuren, Germany

    Andrea Bohnsack

Authors
  1. Thomas Villmann
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  2. Marika Kaden
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  3. David Nebel
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  4. Andrea Bohnsack
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Corresponding author

Correspondence to Thomas Villmann .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

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

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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© 2016 Springer International Publishing Switzerland

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Villmann, T., Kaden, M., Nebel, D., Bohnsack, A. (2016). Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-39384-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39383-4

  • Online ISBN: 978-3-319-39384-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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