The Springer International Series in Engineering and Computer Science

Learning to Classify Text Using Support Vector Machines

Authors: Joachims, Thorsten

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  • ISBN 978-1-4615-0907-3
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About this book

Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications.

Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.

Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook 117,69 €
price for Spain (gross)
  • ISBN 978-1-4615-0907-3
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • Immediate eBook download after purchase and usable on all devices
  • Bulk discounts available
Hardcover 145,59 €
price for Spain (gross)
Softcover 145,59 €
price for Spain (gross)
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Bibliographic Information

Bibliographic Information
Book Title
Learning to Classify Text Using Support Vector Machines
Authors
Series Title
The Springer International Series in Engineering and Computer Science
Series Volume
668
Copyright
2002
Publisher
Springer US
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4615-0907-3
DOI
10.1007/978-1-4615-0907-3
Hardcover ISBN
978-0-7923-7679-8
Softcover ISBN
978-1-4613-5298-3
Series ISSN
0893-3405
Edition Number
1
Number of Pages
XVII, 205
Topics