Learning to Classify Text Using Support Vector Machines

  • ThorstenĀ Joachims

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Introduction

    1. Thorsten Joachims
      Pages 1-6
  3. Text Classification

    1. Thorsten Joachims
      Pages 7-33
  4. Support Vector Machines

    1. Thorsten Joachims
      Pages 35-44
  5. Part Theory

  6. Part Methods

    1. Thorsten Joachims
      Pages 103-117
    2. Thorsten Joachims
      Pages 119-140
  7. Part Algorithms

    1. Thorsten Joachims
      Pages 141-162
    2. Thorsten Joachims
      Pages 163-174
    3. Thorsten Joachims
      Pages 175-179
  8. Back Matter
    Pages 181-205

About this book


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.


Support Vector Machine algorithms classification cognition computer science information learning learning theory machine learning natural language pattern recognition performance

Authors and affiliations

  • ThorstenĀ Joachims
    • 1
  1. 1.Cornell UniversityUSA

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5298-3
  • Online ISBN 978-1-4615-0907-3
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site