Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms [Hardcover]

Thorsten Joachims (Author)

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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: 2.4 x 1.7 x 0.2 cm
Shipping Weight: 408 g
Amazon Bestsellers Rank: #1,585,795 in Books (See Top 100 in Books)
- #100 in Books > Computers & Internet > Business & Culture > Technical Support

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Product 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.

Book Info

Presents a new approach to generating text classifiers from examples. Combines high performance and efficiency with theoretical understanding and improved relationship in particular, and gives a complete and detailed explanation of the SVM approach to learning text classifiers.

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

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Amazon.com: 5.0 out of 5 stars (1 customer review)

3 of 3 people found the following review helpful

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

By S. Purpura - Published on Amazon.com

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This review is from: Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (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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