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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)
 
 
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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)

~ (Author) "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..." (more)
Key Phrases: learning text classifiers, transductive approach, transductive setting, Nervous System, Chunking Minimum, Cornell University (more...)
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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) + Foundations of Statistical Natural Language Processing + The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
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  • This item: Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (The Springer International Series in Engineering and Computer Science) by Thorsten Joachims

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Product Description

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.

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 (2 customer reviews)
  • Amazon.com Sales Rank: #1,186,179 in Books (See Bestsellers in Books)

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    #79 in  Books > Computers & Internet > Business & Culture > Technical Support

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More About the Author

Thorsten Joachims
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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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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Books on Related Topics | Concordance | Text Stats
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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2 of 2 people found the following review helpful:
5.0 out of 5 stars The Gold standard, May 16, 2007
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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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1 of 9 people found the following review helpful:
5.0 out of 5 stars Wonderful book on the subject, September 3, 2005
This is a Tesis Work, it contains a review and conmparation of several learners. It focuses mainly on SVM.
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