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Scalable Kernel Systems

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  • First Online:
Artificial Neural Networks — ICANN 2001 (ICANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

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Abstract

Kernel-based systems are currently very popular approaches to supervised learning. Unfortunately, the computational load for training kernel-based systems increases drastically with the number of training data points. Recently, a number of approximate methods for scaling kernel-based systems to large data sets have been introduced. In this paper we investigate the relationship between three of those approaches and compare their performances experimentally.

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References

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Author information

Authors and Affiliations

  1. Corporate Technology, Siemens AG, Otto-Hahn-Ring 6, 81739, München, Germany

    Volker Tresp & Anton Schwaighofer

  2. Institute for Theoretical Computer Science, TU Graz, Inffeldgasse 16b, 8010, Graz, Austria

    Anton Schwaighofer

Authors
  1. Volker Tresp
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  2. Anton Schwaighofer
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Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Tresp, V., Schwaighofer, A. (2001). Scalable Kernel Systems. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_41

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  • DOI: https://doi.org/10.1007/3-540-44668-0_41

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

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