 This is a result of AdaBoost's focus on examples that are repeatedly misclassified.In contrast, BrownBoost effectively "gives up" on examples that are repeatedly misclassified.The core assumption of BrownBoost is that noisy examples will be repeatedly mislabeled by the weak hypotheses and non-noisy examples will be correctly labeled frequently enough to not be "given up on."