 Thus only noisy examples will be "given up on," whereas non-noisy examples will contribute to the final classifier.In turn, if the final classifier is learned from the non-noisy examples, the generalization error of the final classifier may be much better than if learned from noisy and non-noisy examples.The user of the algorithm can set the amount of error to be tolerated in the training set.