Analysis of SVM with Indefinite Kernels

  title={Analysis of SVM with Indefinite Kernels},
  author={Yiming Ying and Colin Campbell and Mark A. Girolami},
The recent introduction of indefinite SVM by Luss and d’Aspremont [15] has effectively demonstrated SVM classification with a non-positive semi-definite kernel (indefinite kernel). This paper studies the properties of the objective function introduced there. In particular, we show that the objective function is continuously differentiable and its gradient can be explicitly computed. Indeed, we further show that its gradient is Lipschitz continuous. The main idea behind our analysis is that the… CONTINUE READING


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Publications referenced by this paper.
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d’Aspremont. Support vector machine classification with indefinite kernels

  • A. R. Luss
  • 2007
Highly Influential
10 Excerpts

Convex optimization

  • S. Boyd, L. Vandenberghe
  • 2004
Highly Influential
4 Excerpts

Introductory Lectures on Convex Optimization: A Basic Course

  • Y. Nesterov
  • 2003
Highly Influential
7 Excerpts

The theory of max-min and its applications to weapons allocation problems, Springer-Verlag

  • J. M. Danskin
  • New York,
  • 1967
Highly Influential
2 Excerpts

An analysis of transformation on non-positive semidefinite similarity matrix for kernel machines

  • G. Wu, Z. Zhang, E. Y. Chang
  • Technical Report,
  • 2005
2 Excerpts

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