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Approximate Location of Relevant Variables under the Crossover Distribution

  • Peter Damaschke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2264)

Abstract

Searching for genes involved in traits (e.g. diseases), based on genetic data, is considered from a computational learning perspective. This leads to the problem of learning relevant variables of functions from data sampled from a certain class of distributions generalizing the uniform distribution. The Fourier transform of Boolean functions is applied to translate the problem into searching for local extrema of certain functions of observables. We work out the combinatorial structure of this approach and illustrate its potential use.

Keywords

Learning from samples relevance Boolean functions Fourier transform crossover distribution genetics local extrema 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Peter Damaschke
    • 1
  1. 1.Mathematical and Computing SciencesChalmers UniversityGöteborgSweden

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