The most persistent cost in performing machine learning is the creation of tagged data for training purposes. Datasets tend not to come with class labels provided due to the circularity of the situation; one needs a trained classification technique to generate class labels, but cannot train the technique without labeled training and test data. As mentioned, tagging data manually or via test processes is one option, but this can be prohibitively time-consuming, costly (particularly for medical tests), challenging to organize, and prone to error (with large or complex datasets). Semi-supervised techniques suggest a better way to break this deadlock.
Semi-supervised learning techniques use both unlabeled and labeled data to create better learning techniques than can be created with either unlabeled or labeled data individually. There is a family of techniques that exists in a space between supervised (with labeled data) and unsupervised (with unlabeled...