Semi-supervised

Semi-supervised learning is an area between the two previously discussed models. In other words, if you are in a situation where you are using a small amount of labeled data in addition to unlabeled data, then you are performing semi-supervised learning. Semi-supervised learning is widely used in real-world applications, such as speech analysis, protein sequence classification, and web content classification. There are many semi-supervised methods, including generative models, low-density separation, and graph-based methods (discrete Markov Random Fields, manifold regularization, and mincut).