 Thus, if the training set is noisy (say 10% of all examples are assumed to be mislabeled), the booster can be told to accept a 10% error rate.Since the noisy examples may be ignored, only the true examples will contribute to the learning process.BrownBoost uses a non-convex potential loss function, thus it does not fit into the AdaBoost framework.