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Improving the Structuring Capabilities of Statistics–Based Local Learners

  • Slobodan Vukanović
  • Robert Haschke
  • Helge Ritter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8249)

Abstract

Function approximation, a mainstay of machine learning, is a useful tool in science and engineering. Local learning approches subdivide the learning space into regions to be approximated locally by linear models. An arrangement of regions that conforms to the structure of the target function leads to learning with fewer resources and gives an insight into the function being approximated. This paper introduces a covariance–based update for the size and shape of each local region. An evaluation shows that the method improves the structuring capabilities of state–of–the–art statistics–based local learners.

Keywords

Function Approximation Locally Weighted Regression Input Space Structuring 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Slobodan Vukanović
    • 1
  • Robert Haschke
    • 1
  • Helge Ritter
    • 1
  1. 1.Cognitive Interaction Technology – Center of Excellence (CITEC)Bielefeld UniversityBielefeldGermany

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