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In supervised learning tasks, boosting can combine multiple weak learners into a stronger one. AdaBoost is one of the most popular boosting algorithms, which is widely used and stimulates extensive research efforts in the boosting research community. Different from supervised learning, semi-supervised learning aims to make full use of both labeled and unlabeled data to improve learning performance,...
Real Adaboost ensembles with weighted emphasis (RA-we) on erroneous and critical (near the classification boundary) samples have recently been proposed, leading to improved performance when an adequate combination of these terms is selected. However, finding the optimal emphasis adjustment is not an easy task.In this paper, we propose to make a fusion of the outputs of RA-we ensembles trained with...
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