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On qualitative robustness of support vector machines

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  • Hable, Robert
  • Christmann, Andreas
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    Abstract

    Support vector machines (SVMs) have attracted much attention in theoretical and in applied statistics. The main topics of recent interest are consistency, learning rates and robustness. We address the open problem whether SVMs are qualitatively robust. Our results show that SVMs are qualitatively robust for any fixed regularization parameter [lambda]. However, under extremely mild conditions on the SVM, it turns out that SVMs are not qualitatively robust any more for any null sequence [lambda]n, which are the classical sequences needed to obtain universal consistency. This lack of qualitative robustness is of a rather theoretical nature because we show that, in any case, SVMs fulfill a finite sample qualitative robustness property. For a fixed regularization parameter, SVMs can be represented by a functional on the set of all probability measures. Qualitative robustness is proven by showing that this functional is continuous with respect to the topology generated by weak convergence of probability measures. Combined with the existence and uniqueness of SVMs, our results show that SVMs are the solutions of a well-posed mathematical problem in Hadamard's sense.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 102 (2011)
    Issue (Month): 6 (July)
    Pages: 993-1007

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    Handle: RePEc:eee:jmvana:v:102:y:2011:i:6:p:993-1007

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    Related research

    Keywords: Classification Machine learning Nonparametric regression Qualitative robustness Support vector machines;

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    1. repec:cup:cbooks:9780521496032 is not listed on IDEAS
    2. Bartlett, Peter L. & Jordan, Michael I. & McAuliffe, Jon D., 2006. "Convexity, Classification, and Risk Bounds," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 138-156, March.
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    Cited by:
    1. Christmann, Andreas & Hable, Robert, 2012. "Consistency of support vector machines using additive kernels for additive models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 854-873.

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