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A kernel extension to handle missing data

  • Guillermo Nebot-Troyano
  • Lluís A. Belanche-Muñoz
Conference paper

Abstract

An extension for univariate kernels that deals with missing values is proposed. These extended kernels are shown to be valid Mercer kernels and can adapt to many types of variables, such as categorical or continuous. The proposed kernels are tested against standard RBF kernels in a variety of benchmark problems showing different amounts of missing values and variable types. Our experimental results are very satisfactory, because they usually yield slight to much better improvements over those achieved with standard methods.

Keywords

Waste Water Treatment Plant Normalize Root Mean Square Error Kernel Machine Univariate Kernel Kernel Extension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2010

Authors and Affiliations

  • Guillermo Nebot-Troyano
    • 1
  • Lluís A. Belanche-Muñoz
    • 1
  1. 1.Faculty of Computer Science, Polytechnical University of CataloniaBarcelonaSpain

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