Context-Aware Regression from Distributed Sources

  • Héctor Allende-Cid
  • Claudio Moraga
  • Héctor Allende
  • Raúl Monge
Part of the Studies in Computational Intelligence book series (SCI, volume 511)

Abstract

In this paper we present a distributed regression framework to model data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of statistical divergence.We conduct experiments with synthetic data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.

Keywords

Distributed Regression Context-aware Regression Divergence Measures 

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References

  1. 1.
    Allende-Cid, H., Moraga, C., Allende, H., Monge, R.: Regression from distributed sources with different underlying laws of probability. Technical Report, European Centre for Soft Computing, Mieres, Asturias, Spain (available upon request, 2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Héctor Allende-Cid
    • 1
  • Claudio Moraga
    • 2
    • 3
  • Héctor Allende
    • 1
  • Raúl Monge
    • 1
  1. 1.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.European Centre for Soft ComputingMieresSpain
  3. 3.TU Dortmund UniversityDortmundGermany

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