Semi-supervised Learning (Adaptive Computation And Machine Learning)

ISBN 10: 0262033585 / ISBN 13: 9780262033589

Published by Mit Pr, 2006

About this Item

Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.

Seller Inventory # MERV.0262033585.A

Bibliographic Details

• Title: Semi-supervised Learning (Adaptive Computation And Machine Learning)
• Publisher: Mit Pr
• Publication Date: 2006
• Language: English
• ISBN 10: 0262033585
• ISBN 13: 9780262033589
• Binding: Hardcover
• Condition: acceptable

About this title

Synopsis

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.

About the Author

Alexander Zien is Senior Analyst in Bioinformatics atLIFE Biosystems GmbH, Heidelberg.