Learning to Classify Text Using Support Vector Machines
Authors: Joachims, Thorsten
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- About this book
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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)
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Introduction
Pages 1-6
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Text Classification
Pages 7-33
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Support Vector Machines
Pages 35-44
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A Statistical Learning Model of Text Classification for SVMs
Pages 45-74
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Efficient Performance Estimators for SVMs
Pages 75-102
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Table of contents (10 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Learning to Classify Text Using Support Vector Machines
- Authors
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- Thorsten Joachims
- 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
