Concretely, if we find that the current classifier already has acceptably strong confidence on its classification results for all remained unlabeled data, we assume the current training data is sufficient to train the classifier with maximum effectiveness.
if a classifier induced from the current training data has strong classification confidence on an unlabeled example, we could consider it as a redundant example.