Higher Order Functions for Kernel Regression

  • Alexandros Agapitos
  • James McDermott
  • Michael O’Neill
  • Ahmed Kattan
  • Anthony Brabazon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)

Abstract

Kernel regression is a well-established nonparametric method, in which the target value of a query point is estimated using a weighted average of the surrounding training examples. The weights are typically obtained by applying a distance-based kernel function, which presupposes the existence of a distance measure. This paper investigates the use of Genetic Programming for the evolution of task-specific distance measures as an alternative to Euclidean distance. Results on seven real-world datasets show that the generalisation performance of the proposed system is superior to that of Euclidean-based kernel regression and standard GP.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alexandros Agapitos
    • 1
  • James McDermott
    • 2
  • Michael O’Neill
    • 1
  • Ahmed Kattan
    • 3
  • Anthony Brabazon
    • 2
  1. 1.School of Computer Science and InformaticsUniversity College DublinIreland
  2. 2.School of BusinessUniversity College DublinIreland
  3. 3.Dept. of Computer ScienceUm Al Qura UniversityKingdom of Saudi Arabia

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