The intuition behind the B&M co-training algorithm is to train two classifiers that can help augment each other's labeled data by exploiting two separate but redundant views of the data.
each classifier is trained using one view of the labeled data and predicts labels for all instances in the data pool, which consists of a randomly chosen subset of the unlabeled data.