Adaptive classifier integration for robust pattern recognition
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 10834419Full text not available from this repository.
Abstract or description
The integration of multiple classifiers promises higher classification accuracy and robustness than can be obtained with a single classifier. This paper proposes a new 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:||G400 Computer Science|
|Faculty:||Faculty of Computing, Engineering and Sciences > Computing|
|Depositing User:||Claude CHIBELUSHI|
|Date Deposited:||10 Apr 2013 19:18|
|Last Modified:||10 Apr 2013 19:18|
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