Adaptive classifier integration for robust pattern recognition

Chibelushi, Claude and Deravi, Farzin and 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. (The full text of this publication is not available from this repository)

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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
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts
Depositing User: M. Nasiriavanaki
Date Deposited: 26 Jun 2009 07:32
Last Modified: 19 Jun 2014 11:07
Resource URI: http://kar.kent.ac.uk/id/eprint/17206 (The current URI for this page, for reference purposes)
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