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Robust Cross-Validation Score Function for Non-linear Function Estimation

  • Jos De Brabanter
  • Kristiaan Pelckmans
  • Johan A. K. Suykens
  • Joos Vandewalle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)

Abstract

In this paper a new method for tuning regularisation parameters or other hyperparameters of a learning process (non-linear function estimation) is proposed, called robust cross-validation score function \( \left( {CV_{S - {\mathbf{ }}fold}^{Robust} } \right) \) . \( CV_{S - {\mathbf{ }}fold}^{Robust} \) is effective for dealing with outliers and non-Gaussian noise distributions on the data. Illustrative simulation results are given to demonstrate that the \( CV_{S - {\mathbf{ }}fold}^{Robust} \)method outperforms other cross-validation methods.

Keywords

Weighted LS-SVM Robust Cross-Validation Score function Influence functions Breakdown point M-estimators and L-estimators 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jos De Brabanter
    • 1
  • Kristiaan Pelckmans
    • 1
  • Johan A. K. Suykens
    • 1
  • Joos Vandewalle
    • 1
  1. 1.K.U.Leuven ESAT-SCD/SISTALeuvenBelgium

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