Chibelushi, C.C. and Deravi, F. and Mason, J.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.
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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.
|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:||26 Jun 2009 07:32|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/17206 (The current URI for this page, for reference purposes)|
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