Boosting classification performance via data fusion

Costin Barbu, Jing Peng

Research output: Contribution to journalConference article

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
Article number7131102
Pages (from-to)783-787
Number of pages5
JournalIEEE National Radar Conference - Proceedings
Volume2015-June
Issue numberJune
DOIs
Publication statusPublished - 22 Jun 2015
Event2015 IEEE International Radar Conference, RadarCon 2015 - Arlington, United States
Duration: 10 May 201515 May 2015

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Keywords

  • boosting
  • classification
  • data fusion
  • radar

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