Boosting classification performance via data fusion

Costin Barbu, Jing Peng

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

Abstract

An engine for fusing data from multiple sensors for classification is provided in this paper. Two novel methods for fusing multiple representations of data with boosting are presented and empirically evaluated against other fusion techniques as candidate algorithms for the fusion engine. We argue that information fusion from sensors operating in complementary regions of the spectrum and/or spatially separated can improve the classification performance.

Original languageEnglish
Title of host publication2015 IEEE International Radar Conference, RadarCon 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages783-787
Number of pages5
EditionJune
ISBN (Electronic)9781479982325
DOIs
StatePublished - 22 Jun 2015
Event2015 IEEE International Radar Conference, RadarCon 2015 - Arlington, United States
Duration: 10 May 201515 May 2015

Publication series

NameIEEE National Radar Conference - Proceedings
NumberJune
Volume2015-June
ISSN (Print)1097-5659

Other

Other2015 IEEE International Radar Conference, RadarCon 2015
Country/TerritoryUnited States
CityArlington
Period10/05/1515/05/15

Keywords

  • boosting
  • classification
  • data fusion
  • radar

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