MIT Press
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 Schölkopf is Professor and Director at the Max Planck Institute for
Biological Cybernetics in Tübingen. Schölkopf 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.
Chapelle / Schölkopf / Zien
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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 Schölkopf is Professor and Director at the Max Planck Institute for
Biological Cybernetics in Tübingen. Schölkopf 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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