Semi-supervised Learning (Adaptive Computation And Machine Learning)
Language: English
Published by Mit Pr, 2006
- Hardcover
- Used
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Binding is unblemished, text block is clean, boards straight, without highlights or markings. Mild rubbing/chipping to dust wrapper edges. Very clean, nearly like new. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.
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- Title
- Semi-supervised Learning (Adaptive Computation And Machine Learning)
- Publisher
- Mit Pr
- Publication year
- 2006
- Condition
- Very Good
- Dust jacket
- Dust Jacket Included
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0262033585
- ISBN 13
- 9780262033589
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research.
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.
"Synopsis" may belong to another edition of this title.
About the Author
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (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.
Alexander Zien is Senior Analyst in Bioinformatics at LIFE Biosystems GmbH, Heidelberg.
"About the title" may belong to another edition of this title.
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