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). Interest in                  SSL has increased in recent years, particularly because of application domains in                  which unlabeled data are plentiful, such as images, text, and bioinformatics. This                  first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy                  of the field, selected applications, benchmark experiments, and perspectives on                  ongoing and future research.Semi-Supervised Learning first presents the key                  assumptions and ideas underlying the field: smoothness, cluster or low-density                  separation, manifold structure, and transduction. The core of the book is the                  presentation of SSL methods, organized according to algorithmic strategies. After an                  examination of generative models, the book describes algorithms that implement the                  low-density separation assumption, graph-based methods, and algorithms that perform                  two-step learning. The book then discusses SSL applications and offers guidelines                  for SSL practitioners by analyzing the results of extensive benchmark experiments.                  Finally, the book looks at interesting directions for SSL research. The book closes                  with a discussion of the relationship between semi-supervised learning and                  transduction.Olivier Chapelle and Alexander Zien are Research Scientists and                  Bernhard Sch?lkopf is Professor and Director at the Max Planck Institute for                  Biological Cybernetics in T?bingen. Sch?lkopf is coauthor of Learning with Kernels                  (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector                  Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in                  Computational Biology (2004), all published by The MIT Press.

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