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Semi-Supervised Learning

Online ISBN:
9780262255899
Print ISBN:
9780262033589
Publisher:
The MIT Press
Book

Semi-Supervised Learning

Olivier Chapelle (ed.),
Olivier Chapelle
(ed.)
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Bernhard Scholkopf (ed.),
Bernhard Scholkopf
(ed.)
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Alexander Zien (ed.)
Alexander Zien
(ed.)
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Published:
22 September 2006
Online ISBN:
9780262255899
Print ISBN:
9780262033589
Publisher:
The MIT Press

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 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. It 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 which perform two-step learning. It then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of benchmark experiments. Finally, the book looks at interesting directions for SSL research. It closes with a discussion of the relationship between semi-supervised learning and transduction.

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