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In this paper, we propose a novel method that aims at assisting the ground-truth expert through an automatic detection of potentially mislabeled learning samples. This method is based on viewing the mislabeled sample detection issue as an optimization problem where it is looked for the best subset of learning samples in terms of statistical separability between classes. This problem is formulated...
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