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Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance
To overcome this dilemma, we take a different perspective and investigate a new approach to handle inaccuracy and uncertainty in the training data labels, which are inevitable in many applications where labels are conceptual and labeling is performed by human annotators.
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