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Learning to Classify Text Using Support Vector Machines

Learning to Classify Text Using Support Vector Machines

Thorsten Joachims
Springer US
Ebook Taschenbuch
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ISBN-13:
9780792376798
Veröffentl:
2002
Einband:
HC runder Rücken kaschiert
Erscheinungsdatum:
30.04.2002
Seiten:
224
Autor:
Thorsten Joachims
Gewicht:
506 g
Format:
241x160x17 mm
Serie:
668, The Springer International Series in Engineering and Computer Science
Sprache:
Englisch
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.

The SVM approach is efficient in training and classification, and it comes with a learning theory that can guide real-world applications. This book gives a description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, and a statistical learning model of text classification.
1. Introduction.- 1 Challenges.- 2 Goals.- 3 Overview and Structure of the Argument.- 4 Summary.- 2. Text Classification.- 1 Learning Task.- 2 Representing Text.- 3 Feature Selection.- 4 Term Weighting.- 5 Conventional Learning Methods.- 6 Performance Measures.- 7 Experimental Setup.- 3. Support Vector Machines.- 1 Linear Hard-Margin SVMs.- 2 Soft-Margin SVMs.- 3 Non-Linear SVMs.- 4 Asymmetric Misclassification Cost.- 5 Other Maximum-Margin Methods.- 6 Further Work and Further Information.- Theory.- 4. A Statistical Learning Model of text Classification for SVMs.- 5. Efficient Performance Estimators for SVMs.- Methods.- 6. Inductive Text Classification.- 7. Transductive Text Classification.- Algorithms.- 8. Training Inductive Support Vector Machines.- 9. Training Transductive Support Vector Machines.- 10. Conclusions.- Appendices.- SVM-Light Commands and Options.

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