Context-aware regression from distributed sources

Héctor Allende-Cid, Claudio Moraga, Héctor Allende, Raúl Monge

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaIntelligent Distributed Computing VII
Subtítulo de la publicación alojadaProceedings of the 7th International Smposium on Intelligent Distributed Computing - IDC 2013, Prague, Czech Republi
EditorialSpringer Verlag
Páginas17-22
Número de páginas6
ISBN (versión impresa)9783319015705
DOI
EstadoPublicada - 2014
Publicado de forma externa

Serie de la publicación

NombreStudies in Computational Intelligence
Volumen511
ISSN (versión impresa)1860-949X

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