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  Semi-Supervised Learning

Chapelle, O., Schölkopf, B., & Zien, A. (2006). Semi-Supervised Learning. Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D029-E Version Permalink: http://hdl.handle.net/21.11116/0000-0004-9D07-4
Genre: Book

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 Creators:
Chapelle, O1, 2, Author              
Schölkopf, B1, 2, Author              
Zien, A1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2006-09
 Publication Status: Published in print
 Pages: 508
 Publishing info: Cambridge, MA, USA : MIT Press
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 3603
ISBN: 0-262-03358-5
DOI: 10.7551/mitpress/9780262033589.001.0001
 Degree: -

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Title: Adaptive computation and machine learning
Source Genre: Series
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -