Context-aware regression from distributed sources

HÉCTOR GABRIEL ALLENDE CID, Claudio Moraga, Héctor Allende, Raúl Monge

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationIntelligent Distributed Computing VII
Subtitle of host publicationProceedings of the 7th International Smposium on Intelligent Distributed Computing - IDC 2013, Prague, Czech Republi
PublisherSpringer Verlag
Pages17-22
Number of pages6
ISBN (Print)9783319015705
DOIs
StatePublished - 2014
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume511
ISSN (Print)1860-949X

Keywords

  • Context-aware Regression
  • Distributed Regression
  • Divergence Measures

Fingerprint Dive into the research topics of 'Context-aware regression from distributed sources'. Together they form a unique fingerprint.

Cite this