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Adaptive classifier integration for robust pattern recognition

Chibelushi, Claude, Deravi, Farzin, Mason, John S.D. (1999) Adaptive classifier integration for robust pattern recognition. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, 29 (6). pp. 902-907. ISSN 1083-4419. (doi:10.1109/3477.809043) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:17206)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1109/3477.809043

Abstract

The integration of multiple classifiers promises higher classification accuracy and robustness than can be obtained with a single classifier. This paper proposes a ne rv adaptive technique for classifier integration based on a linear combination model. The proposed technique is shown to exhibit robustness to a mismatch between test and training conditions. It often outperforms the most accurate of the fused information sources. A comparison between adaptive linear combination and non-adaptive Bayesian fusion shows that, under mismatched test and training conditions, the former is superior to the latter in terms of identification accuracy and insensitivity to information source distortion.

Item Type: Article
DOI/Identification number: 10.1109/3477.809043
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: M. Nasiriavanaki
Date Deposited: 26 Jun 2009 07:32 UTC
Last Modified: 16 Nov 2021 09:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/17206 (The current URI for this page, for reference purposes)

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