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Optimization over Zonotopes and Training Support Vector Machines

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Algorithms and Data Structures (WADS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2125))

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Abstract

We make a connection between classical polytopes called zonotopes and Support Vector Machine (SVM) classifiers. We combine this connection with the ellipsoid method to give some new theoretical results on training SVMs. We also describe some special properties of C-SVMs for C → ∞.

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Author information

Authors and Affiliations

  1. Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CA, 94304

    Marshall Bern

  2. Dept. Inf. & Comp. Sci., Univ. of California, Irvine, Irvine, CA, 92697

    David Eppstein

Authors
  1. Marshall Bern
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  2. David Eppstein
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Editor information

Editors and Affiliations

  1. School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Candada, K1S 5B6

    Frank Dehne  & Jörg-Rüdiger Sack  & 

  2. Center for Geometric Computing, Brown University, Providence, RI, 02912-1910, USA

    Roberto Tamassia

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© 2001 Springer-Verlag Berlin Heidelberg

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Bern, M., Eppstein, D. (2001). Optimization over Zonotopes and Training Support Vector Machines. In: Dehne, F., Sack, JR., Tamassia, R. (eds) Algorithms and Data Structures. WADS 2001. Lecture Notes in Computer Science, vol 2125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44634-6_11

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  • DOI: https://doi.org/10.1007/3-540-44634-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42423-9

  • Online ISBN: 978-3-540-44634-7

  • eBook Packages: Springer Book Archive

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