Semi-supervised learning

Semi-supervised learning is a technique in between supervised and unsupervised learning. Arguably, it should not be a category of machine learning but only a generalization of supervised learning, but it's useful to introduce the concept separately.

Its aim is to reduce the cost of gathering labeled data by extending a few labels to similar unlabeled data. Some generative models are classified semi-supervised approaches.

Semi-supervised learning can be divided into transductive and inductive learning. Transductive learning is when we want to infer the labels for unlabeled data. The goal of inductive learning is to infer the correct mapping from inputs to outputs.

We can see this process as similar to most of the learning we had at school. The teacher shows the students a few examples and gives them some to take home; to solve those, they need to generalize.