Semi-Supervised Learning

Semi-Supervised Learning

by Olivier Chapelle
     
 

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

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Overview

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.

The MIT Press

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Editorial Reviews

From the Publisher
"In summary, reading this book is a delightful journey through semi-supervised learning." Hsun-Hsien Chang Computing Reviews
Computing Reviews - Hsun-Hsien Chang

In summary, reading this book is a delightful journey through semi-supervised learning.

Product Details

ISBN-13:
9780262033589
Publisher:
MIT Press
Publication date:
09/01/2006
Series:
Adaptive Computation and Machine Learning series
Pages:
528
Product dimensions:
8.00(w) x 10.00(h) x 1.00(d)
Age Range:
18 Years

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What People are saying about this

From the Publisher
"In summary, reading this book is a delightful journey through semi-supervised learning." Hsun-Hsien Chang Computing Reviews

The MIT Press

Meet the Author

Olivier Chapelle is Senior Research Scientist in Machine Learning at Yahoo.

Bernhard Schölkopf is Professor and Director at the Max Planck Institute for
Biological Cybernetics 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.

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