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Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (The Springer International Series in Engineering and Computer Science) [Hardcover]

Thorsten Joachims (Author)

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Book Description

Publication Date: April 30, 2002 | ISBN-10: 079237679X | ISBN-13: 978-0792376798 | Edition: 1

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.

#outer_postBodyPS { display: none; } #psGradient { display: none; } #psPlaceHolder { display: none; } #psExpand { display: none; } 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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Product Details

Hardcover: 228 pages
Publisher: Springer; 1 edition (April 30, 2002)
Language: English
ISBN-10: 079237679X
ISBN-13: 978-0792376798
Product Dimensions: 9.2 x 6.1 x 0.8 inches
Shipping Weight: 14.4 ounces (View shipping rates and policies)

Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)

Amazon Best Sellers Rank: #1,915,491 in Books (See Top 100 in Books)

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5.0 out of 5 stars The Gold standard, May 16, 2007

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This review is from: Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (The Springer International Series in Engineering and Computer Science) (Hardcover)

This is a must read for anyone beginning to investigate the analysis of meaning in text using computational methods. I found the initial sections were useful in bringing together my thought on many different aspects of the topic.

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Inside This Book (learn more)

First Sentence:
With the rapid growth of the World Wide Web, the task of classifying natural language documents into a predefined set of semantic categories has become one of the key methods for organizing online information. Read the first page

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
learning text classifiers, transductive approach, transductive setting, transductive support vector machines, stopword removal, inverse margin, transductive learning, expected generalization error, inductive setting, little training data, average training time, statistical learning model, binary tasks, large test sets, text classification, test set size, negative documents, unlabeled examples, negative training examples, unlabeled data, positive training examples, transductive inference, discriminative features, small training sets, generalization performance

Key Phrases - Capitalized Phrases (CAPs): (learn more)
Nervous System, Chunking Minimum, Cornell University, Examples Examples Figure, World Wide Web

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Citations (learn more)

This book cites 19 books:

Machine Learning by Tom M. Mitchell on 6 pages
Information Retrieval by C. J. Van Rijsbergen on 4 pages
Research and Development in Information Retrieval (British Computer Society Workshop Series) by C. J. van Rijsbergen in Back Matter (1), Back Matter (2), and Back Matter (3)
Machine Learning: Progress and Prospects (Inaugural Lecture) by A. Gammerman in Back Matter (1), and Back Matter (2)
An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Bradley Efron in Back Matter

See all 19 books this book cites

Books on Related Topics (learn more)

Advances in Knowledge Discovery and Data Mining by Honghua Dai

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Advances in Web-Age Information Management by Wenfei Fan

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Encyclopedia of Data Warehousing and Mining by John Wang

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