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Indefinite Support Vector Regression

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Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10614))

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Abstract

Non-metric proximity measures got wide interest in various domains such as life sciences, robotics and image processing. The majority of learning algorithms for these data are focusing on classification problems. Here we derive a regression algorithm for indefinite data representations based on the support vector machine. The approach avoids heuristic eigen spectrum modifications or costly proxy matrix approximations, as used in general. We evaluate the method on a number of benchmark data using an indefinite measure.

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Notes

  1. 1.

    A mathematical construct detailed in [16].

  2. 2.

    Details are skipped due to lack of space, but follow analogous to the derivation of the Krein SVM. Instead we provide an adapted pseudo code with descriptions.

  3. 3.

    Note: iSVR can be applied on non-psd as well as on psd kernels.

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Author information

Authors and Affiliations

  1. University of Applied Sciences Wuerzburg-Schweinfurt, 97074, Wuerzburg, Germany

    Frank-Michael Schleif

  2. School of Computer Science Edgbaston, University of Birmingham, Birmingham, B15 2TT, UK

    Frank-Michael Schleif

Authors
  1. Frank-Michael Schleif
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Correspondence to Frank-Michael Schleif .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Schleif, FM. (2017). Indefinite Support Vector Regression. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_36

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  • DOI: https://doi.org/10.1007/978-3-319-68612-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68611-0

  • Online ISBN: 978-3-319-68612-7

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