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Consistency and robustness of kernel based regression

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  • Christmann, Andreas
  • Steinwart, Ingo

Abstract

We investigate properties of kernel based regression (KBR) methods which are inspired by the convex risk minimization method of support vector machines. We first describe the relation between the used loss function of the KBR method and the tail of the response variable Y . We then establish a consistency result for KBR and give assumptions for the existence of the influence function. In particular, our results allow to choose the loss function and the kernel to obtain computational tractable and consistent KBR methods having bounded influence functions. Furthermore, bounds for the sensitivity curve which is a finite sample version of the influence function are developed, and some numerical experiments are discussed.

Suggested Citation

  • Christmann, Andreas & Steinwart, Ingo, 2005. "Consistency and robustness of kernel based regression," Technical Reports 2005,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200501
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    1. Struyf, Anja J. & Rousseeuw, Peter J., 1999. "Halfspace Depth and Regression Depth Characterize the Empirical Distribution," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 135-153, April.
    2. Tomaso Poggio & Ryan Rifkin & Sayan Mukherjee & Partha Niyogi, 2004. "General conditions for predictivity in learning theory," Nature, Nature, vol. 428(6981), pages 419-422, March.
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    Cited by:

    1. Marin-Galiano, Marcos & Luebke, Karsten & Christmann, Andreas & Rüping, Stefan, 2005. "Determination of hyper-parameters for kernel based classification and regression," Technical Reports 2005,38, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2006. "Robust Learning from Bites for Data Mining," Technical Reports 2006,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Jorge Daniel Mello-Román & Adolfo Hernández & Julio César Mello-Román, 2021. "Improved Predictive Ability of KPLS Regression with Memetic Algorithms," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
    4. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.

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