Semi-supervised learning

While we have discussed the three major types of ML, there exists yet another type, which is semi-supervised learning. By the name of the term, you could guess that it would have to do something with a mix of labeled and unlabeled training samples. In most cases, the number of unlabeled training samples exceeds the number of labeled samples.

Semi-supervised learning has been used successfully to produce more efficient results when some labeled samples are added to a problem entirely belonging to unsupervised learning. Also, since only a few samples are labeled, the complexity of supervised learning is avoided. With this approach, we can produce better results than we would get from a purely unsupervised learning system and incur lesser computational cost than a pure supervised learning system.