Learning of Decision Fusion Mappings for Pattern Recognition
نویسندگان
چکیده
Different learning algorithms for the decision fusion mapping of a multiple classifier system are compared in this paper. It is very well known that the confusion matrices of the individual classifiers are utilised in the naive Bayes combination of classifier outputs. After a brief review of the decision templates, the linear associative memory and the pseudoinverse matrix approaches it is demonstrated that all four adaptive decision fusion mappings share the confusion matrices as the essential ingredient.
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