Learning to Classify Text Using Support Vector Machines / Edition 1

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More About This Textbook

Overview

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.
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Editorial Reviews

From The Critics
Provides a thorough, detailed description of the Support Vector Machines (SVMs) approach to learning text classifiers including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. An overview of the field of text classification makes it useful for newcomers to the field. Includes a concise introduction to SVMs for pattern recognition, with a detailed description of how to formulate text-classification tasks for machine learning. Intended as both a secondary text for graduate students in computer science and as a reference for researchers and practitioners. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

Foreword; T.Mitchell, K. Morik. Preface. Acknowledgments. Notation. 1. Introduction. 2. Text Classification. 3. Support Vector Machines. Part Theory. 4. A Statistical Learning Model of Text Classification for SVMS. 5. Efficient Performance Estimators for SVMS. Part Methods. 6. Inductive Text Classification. 7. Transductive Text Classification. Part Algorithms. 8. Training Inductive Support Vector Machines. 9. Training Transductive Support Vector Machines. 10. Conclusions. Bibliography. Appendices. Index.
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  • Anonymous

    Posted February 11, 2011

    Highly Recommended - you must check it out

    Learning to classify text using support vector machines

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