title: Adaptive classifier integration for robust pattern recognition.
abstract: 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.
authors.affiliation: Sch. of Comput., Staffordshire Polytech., Stafford.
authors.forename: C C
authors.initials: CC
authors.lastname: Chibelushi
authors.forename: F
authors.initials: F
authors.lastname: Deravi
authors.forename: J D
authors.initials: JD
authors.lastname: Mason