Learning to Classify Text Using Support Vector Machines

2002
ISBN: 978-0-7923-7679-8
Verlag: Springer US

Buch, Englisch, Band 668, 205 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 1100 g

Reihe: The Springer International Series in Engineering and Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Joachims, Thorsten

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Zeichen- und Zahlendarstellungen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate

Inhaltsverzeichnis

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.