Boosting in classifier fusion vs. fusing boosted classifiers

Costin Barbu, Kun Zhang, Jing Peng, Bill Buckles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper we investigate the performance of boosting used for fusing various classifiers. We propose a new boosting - based algorithm for fusion and we show through empirical studies on texture image data sets that it outperforms existing SVM-based classifier fusion technique in terms of accuracy, computational efficiency and robustness.

Original languageEnglish
Title of host publicationProceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
Pages332-337
Number of pages6
Volume2005
DOIs
StatePublished - 1 Dec 2005
Event2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005 - Las Vegas, NV, United States
Duration: 15 Aug 200517 Aug 2005

Other

Other2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
CountryUnited States
CityLas Vegas, NV
Period15/08/0517/08/05

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  • Cite this

    Barbu, C., Zhang, K., Peng, J., & Buckles, B. (2005). Boosting in classifier fusion vs. fusing boosted classifiers. In Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005 (Vol. 2005, pp. 332-337). [1506495] https://doi.org/10.1109/IRI-05.2005.1506495