Learning to Classify Text Using Support Vector Machines

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Springer Science & Business Media, Apr 30, 2002 - Computers - 205 pages
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Text Classification, or the task of automatically assigning semantic categories to natural language text, has become one of the key methods for organizing online information. Since hand-coding classification rules is costly or even impractical, most modern approaches employ machine learning techniques to automatically learn text classifiers from examples. However, none of these conventional approaches combines good prediction performance, theoretical understanding, and efficient training algorithms.

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.

Learning To Classify Text Using Support Vector Machines is designed as a reference for researchers and practitioners, and is suitable as a secondary text for graduate-level students in Computer Science within Machine Learning and Language Technology.

  

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LibraryThing Review

User Review  - juha - LibraryThing

This is a thorough representation of using support vector machines in text categorization. Linear kernels did okay. The SVM Light classifier is downloadable from Joachim's site. Read full review

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Common terms and phrases

Popular passages

Page 193 - Salton, G., editor, The SMART Retrieval System — Experiments in Automatic Document Processing, chapter 14, pages 313-323.
Page 196 - J. Weston and C. Watkins. Multi-class support vector machines. Technical Report CSD-TR-98-04, Royal Holloway University of London, 1998.
Page 196 - Fisher, editor, Proceedings of ICML-97, 14th International Conference on Machine Learning, pages 412-420, Nashville, US, 1997.
Page 192 - Latent semantic indexing: A probabilistic analysis. In ACM, editor, PODS '98. Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1-3, 1998.
Page 192 - M (1980) An Algorithm for Suffix Stripping. Program: Automated Library and Information Systems, 14(3).
Page 193 - Zobel, editors, Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval, pages 215-223, Melbourne, AU, 1998. ACM Press, New York, US.

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