Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 668)
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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.
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Keywords
Table of contents (10 chapters)
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Front Matter
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Introduction
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Text Classification
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Support Vector Machines
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Learning to Classify Text Using Support Vector Machines
Authors: Thorsten Joachims
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-0907-3
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2002
Hardcover ISBN: 978-0-7923-7679-8Published: 30 April 2002
Softcover ISBN: 978-1-4613-5298-3Published: 01 November 2012
eBook ISBN: 978-1-4615-0907-3Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XVII, 205
Topics: Artificial Intelligence, Information Storage and Retrieval, Data Structures and Information Theory, Information Systems Applications (incl. Internet)