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Learning to Classify Text Using Support Vector Machines

Methods, Theory and Algorithms. 'The Springer International Series in Engineering and Computer Science'.…
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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 u... weiterlesen
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Titel: Learning to Classify Text Using Support Vector Machines
Autor/en: Thorsten Joachims

ISBN: 079237679X
EAN: 9780792376798
Methods, Theory and Algorithms.
'The Springer International Series in Engineering and Computer Science'.
Auflage 2002.
Book.
Sprache: Englisch.
Springer US

30. April 2002 - gebunden - 224 Seiten

Beschreibung

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.

Inhaltsverzeichnis

Foreword; T.Mitchell, K. Morik. Preface. Acknowledgments. Notation.
1. Introduction.
2. Text Classification.
3. Support Vector Machines. Part Theory.
4. A Statistical Learning Model of Text Classification for SVMS.
5. Efficient Performance Estimators for SVMS. Part Methods.
6. Inductive Text Classification.
7. Transductive Text Classification. Part Algorithms.
8. Training Inductive Support Vector Machines.
9. Training Transductive Support Vector Machines.
10. Conclusions. Bibliography. Appendices. Index.
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