A Primer on Semi-Supervised Learning — Part 2

Semi-Supervised Learning (SSL) is an interesting approach towards addressing the lack of labeled data issue in Machine Learning. SSL utilizes unlabeled data along with a labeled dataset to learn a task. The objective of SSL is to perform better than a supervised learning technique trained using labeled data alone. This is Part 2 of the article series on Semi-Supervised Learning and covers a few initial SSL techniques in detail. Part 3 will cover recent SSL approaches and will be published soon. Part 1 gives an introduction to Semi-Supervised Learning and can be found here.