Inductive Inference for Large Scale Text Classification
Kernel Approaches and Techniques
Authors: Silva, Catarina, Ribeiro, Bernadete
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- About this book
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Text classification is becoming a crucial task to analysts in different areas. In the last few decades, the production of textual documents in digital form has increased exponentially. Their applications range from web pages to scientific documents, including emails, news and books. Despite the widespread use of digital texts, handling them is inherently difficult - the large amount of data necessary to represent them and the subjectivity of classification complicate matters.
This book gives a concise view on how to use kernel approaches for inductive inference in large scale text classification; it presents a series of new techniques to enhance, scale and distribute text classification tasks. It is not intended to be a comprehensive survey of the state-of-the-art of the whole field of text classification. Its purpose is less ambitious and more practical: to explain and illustrate some of the important methods used in this field, in particular kernel approaches and techniques.
- Table of contents (6 chapters)
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Background on Text Classification
Pages 3-29
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Kernel Machines for Text Classification
Pages 31-48
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Enhancing SVMs for Text Classification
Pages 51-70
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Scaling RVMs for Text Classification
Pages 71-91
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Distributing Text Classification in Grid Environments
Pages 93-115
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Table of contents (6 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Inductive Inference for Large Scale Text Classification
- Book Subtitle
- Kernel Approaches and Techniques
- Authors
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- Catarina Silva
- Bernadete Ribeiro
- Series Title
- Studies in Computational Intelligence
- Series Volume
- 255
- Copyright
- 2010
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-642-04533-2
- DOI
- 10.1007/978-3-642-04533-2
- Hardcover ISBN
- 978-3-642-04532-5
- Softcover ISBN
- 978-3-642-26134-3
- Series ISSN
- 1860-949X
- Edition Number
- 1
- Number of Pages
- XX, 155
- Topics
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