Learning to Classify Text Using Support Vector Machines

By:

Thorsten Joachims

Format:

Paperback / softback

Series:

The Springer International Series in Engineering and Computer Science

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Description

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.

Release date Australia

November 1st, 2012

Author

Thorsten Joachims

Audience

Professional & Vocational

Edition

Softcover reprint of the original 1st ed. 2002

Illustrations

XVII, 205 p.

Pages

205

Series

The Springer International Series in Engineering and Computer Science

Dimensions

156x234x12

ISBN-13

9781461352983

Product ID

21280851

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