In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). After examination of generative models, this book describes algorithms that implement low-density separation assumption, graph-based methods, and learning algorithms that perform two steps. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. The book concludes with a discussion of the relationship between semi-supervised learning and transduction. Adaptive Computation and Machine Learning series