The problem of centralizing distributed data sources in the regression task

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

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

Resumen

In this work we present the effects of centralizing distributed data sources in order to perform automatic data analysis, without taking into account the different underlying laws of probability that these data sources could have. We compare a centralized approach and two distributed approaches for the distributed regression task. The experiments are performed on a set of synthetic and real data sets, in order to validate that the distributed approaches outperform the classic approach. The results indicate that in most cases, the centralized approach yields worse results.

Idioma originalInglés
Título de la publicación alojadaIET Seminar Digest
EditorialInstitution of Engineering and Technology
Edición2
ISBN (versión digital)9781785612220, 9781785612343, 9781785612831, 9781785613104, 9781785613173, 9781785614002, 9781785614019
DOI
EstadoPublicada - 2016
Publicado de forma externa
EventoInternational Conference on Pattern Recognition Systems, ICPRS 2016 - Talca, Chile
Duración: 20 abr. 201622 abr. 2016

Serie de la publicación

NombreIET Seminar Digest
Número2
Volumen2016

Conferencia

ConferenciaInternational Conference on Pattern Recognition Systems, ICPRS 2016
País/TerritorioChile
CiudadTalca
Período20/04/1622/04/16

Huella

Profundice en los temas de investigación de 'The problem of centralizing distributed data sources in the regression task'. En conjunto forman una huella única.

Citar esto