Regularizing AdaBoost to prevent overfitting on label noise

In this work, we have shown that AdaBoost is prone to overfitting when the training set contains mislabeled objects. We proposed that this is in part because the error estimate used to weight base classifiers and (indirectly) objects is biased in this scenario. We have shown that an unbiased estimator can prevent the overfitting, but such an...