Boosting in classifier fusion vs. fusing boosted classifiers

Costin Barbu; Kun Zhang; Jing Peng; Bill Buckles

doi:10.1109/IRI-05.2005.1506495

Boosting in classifier fusion vs. fusing boosted classifiers

Costin Barbu, Kun Zhang, Jing Peng, Bill Buckles

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
Pages	332-337
Number of pages	6
Volume	2005
DOIs	https://doi.org/10.1109/IRI-05.2005.1506495
State	Published - 1 Dec 2005
Event	2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005 - Las Vegas, NV, United States Duration: 15 Aug 2005 → 17 Aug 2005

Other

Other	2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
Country/Territory	United States
City	Las Vegas, NV
Period	15/08/05 → 17/08/05

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10.1109/IRI-05.2005.1506495

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title = "Boosting in classifier fusion vs. fusing boosted classifiers",

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.",

author = "Costin Barbu and Kun Zhang and Jing Peng and Bill Buckles",

year = "2005",

month = dec,

day = "1",

doi = "10.1109/IRI-05.2005.1506495",

language = "English",

isbn = "0780390938",

volume = "2005",

pages = "332--337",

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note = "2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005 ; Conference date: 15-08-2005 Through 17-08-2005",

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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, 1506495, pp. 332-337, 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005, Las Vegas, NV, United States, 15/08/05. https://doi.org/10.1109/IRI-05.2005.1506495

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T1 - Boosting in classifier fusion vs. fusing boosted classifiers

AU - Barbu, Costin

AU - Zhang, Kun

AU - Peng, Jing

AU - Buckles, Bill

PY - 2005/12/1

Y1 - 2005/12/1

N2 - 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.

AB - 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.

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U2 - 10.1109/IRI-05.2005.1506495

DO - 10.1109/IRI-05.2005.1506495

M3 - Conference contribution

AN - SCOPUS:33745728485

SN - 0780390938

SN - 9780780390935

VL - 2005

SP - 332

EP - 337

BT - Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005

T2 - 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005

Y2 - 15 August 2005 through 17 August 2005

ER -

Boosting in classifier fusion vs. fusing boosted classifiers

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