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Kernelized design of experiments

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  • Rüping, Stefan
  • Weihs, Claus

Abstract

This paper describes an approach for selecting instances in regression problems in the cases where observations x are readily available, but obtaining labels y is hard. Given a database of observations, an algorithm inspired by statistical design of experiments and kernel methods is presented that selects a set of k instances to be chosen in order to maximize the prediction performance of a support vector machine. It is shown that the algorithm significantly outperforms related approaches on a number of real-world datasets.

Suggested Citation

  • Rüping, Stefan & Weihs, Claus, 2009. "Kernelized design of experiments," Technical Reports 2009,02, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200902
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    File URL: https://www.econstor.eu/bitstream/10419/36602/1/600486184.PDF
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    1. Pumplün, Constanze & Rüping, Stefan & Morik, Katharina & Weihs, Claus, 2005. "D-optimal plans in observational studies," Technical Reports 2005,44, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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