The problem of centralizing distributed data sources in the regression task

HÉCTOR GABRIEL ALLENDE CID, C. Moraga, R. Monge, H. Allende

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

Abstract

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.

Original languageEnglish
Title of host publicationIET Seminar Digest
PublisherInstitution of Engineering and Technology
Edition2
ISBN (Electronic)9781785612220, 9781785612343, 9781785612831, 9781785613104, 9781785613173, 9781785614002, 9781785614019
DOIs
StatePublished - 2016
Externally publishedYes
EventInternational Conference on Pattern Recognition Systems, ICPRS 2016 - Talca, Chile
Duration: 20 Apr 201622 Apr 2016

Publication series

NameIET Seminar Digest
Number2
Volume2016

Conference

ConferenceInternational Conference on Pattern Recognition Systems, ICPRS 2016
CountryChile
CityTalca
Period20/04/1622/04/16

Keywords

  • Distributed sources
  • Machine learning
  • Regression

Fingerprint Dive into the research topics of 'The problem of centralizing distributed data sources in the regression task'. Together they form a unique fingerprint.

Cite this