Learning to Classify Text Using Support Vector Machines

Joachims, Thorsten

ISBN 10: 079237679X / ISBN 13: 9780792376798

Published by Kluwer Academic Publishers, 2002

About this Item

Based on ideas from Support Vector Machines (SVMs), this title presents an approach to generating text classifiers from examples. This book gives a detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, and, efficient performance estimation. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 205 pages, biography. BIC Classification: UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 14. Weight in Grams: 498. . 2002. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780792376798

Bibliographic Details

• Title: Learning to Classify Text Using Support Vector Machines
• Publisher: Kluwer Academic Publishers
• Publication Date: 2002
• Language: English
• ISBN 10: 079237679X
• ISBN 13: 9780792376798
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.