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AdaBoost was proposed as an efficient algorithm of the ensemble learning field, it selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy can not be avoided. We proposed a post optimization procedure for the found classifiers and their coefficients based...
In this paper, we accomplish matching score level fusion of multi-biometrics. In order to solve the incomparability among different classifiers' outputs, adaptive confidence transform (ACT) is introduced to convert the raw outputs of different classifiers to the estimates of posteriori probabilities conforming to different users. These posteriori probabilities are then combined using several fusion...
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