Context-Aware Regression from Distributed Sources

Allende-Cid, H; Moraga, C.; Allende, H.; Monge R.

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.

Más información

Título según WOS: Context-Aware Regression from Distributed Sources
Título según SCOPUS: Context-aware regression from distributed sources
Título de la Revista: SERVICE ORIENTATION IN HOLONIC AND MULTI-AGENT MANUFACTURING
Volumen: 511
Editorial: SPRINGER-VERLAG BERLIN
Fecha de publicación: 2014
Página de inicio: 17
Página final: 22
Idioma: English
DOI:

10.1007/978-3-319-01571-2_3

Notas: ISI, SCOPUS