Overview
- Presents recent research in inductive inference for Large Scale Text Classification
Part of the book series: Studies in Computational Intelligence (SCI, volume 255)
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About this book
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.
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Keywords
Table of contents (6 chapters)
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Front Matter
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Fundamentals
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Front Matter
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Approaches and techniques
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Inductive Inference for Large Scale Text Classification
Book Subtitle: Kernel Approaches and Techniques
Authors: Catarina Silva, Bernardete Ribeiro
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-04533-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2010
Hardcover ISBN: 978-3-642-04532-5Published: 13 November 2009
Softcover ISBN: 978-3-642-26134-3Published: 14 March 2012
eBook ISBN: 978-3-642-04533-2Published: 24 November 2009
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XX, 155
Topics: Mathematical and Computational Engineering, Natural Language Processing (NLP), Computational Linguistics, Artificial Intelligence