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Adaptive classifier integration for robust pattern recognition

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3 Author(s)
Chibelushi, C.C. ; Sch. of Comput., Staffordshire Polytech., Stafford, UK ; Deravi, F. ; Mason, J.S.D.

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

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:29 ,  Issue: 6 )