Co-training is a semi-supervised learning technique that requires two views of the data. It was introduced by Avrim Blum and Tom Mitchell. It assumes that each example is described using two different feature sets that provide different, complementary information about the instance. Ideally, the two views are conditionally independent (i.e., the two feature sets of each instance are conditionally independent given the class) and each view is sufficient (i.e., the class of an instance can be accurately predicted from each view alone). Co-training first learns a separate classifier for each view using any labeled examples. The most confident predictions of each classifier on the unlabeled data are then used to iteratively construct additional labeled training data.

The original co-training paper received 10 year's Best Paper Award at 25th International Conference on Machine Learning (ICML 2008). ICML is one of the renowned conferences in Computer Science.

Co-training was initially used to classify web-pages using the text on the page as one view and the anchor text of hyperlinks on other pages that point to the page as the other view.

References

Blum, A., Mitchell, T. Combining labeled and unlabeled data with co-training COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann, 1998, p. 92-100.