Learning to Classify Text Using Support Vector Machines by Thorsten Joachims.

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 particula...

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Bibliographic Details
Main Author:	Joachims, Thorsten (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	New York, NY : Springer US : Imprint: Springer, 2002.
Edition:	1st ed. 2002.
Series:	The Springer International Series in Engineering and Computer Science, 668 Springer eBook Collection.
Subjects:	Artificial intelligence. Information storage and retrieval. Data structures (Computer science). Application software. Electronic resources (E-books)
Online Access:	Click to view e-book
Holy Cross Note:	Loaded electronically. Electronic access restricted to members of the Holy Cross Community.

Table of Contents:

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.

Learning to Classify Text Using Support Vector Machines by Thorsten Joachims.

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