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Inductive Inference for Large Scale Text Classification [electronic resource] : Kernel Approaches and Techniques / by Catarina Silva, Bernardete Ribeiro.

By: Silva, Catarina [author.].
Contributor(s): Ribeiro, Bernardete [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 255Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642045332.Subject(s): Engineering | Artificial intelligence | Text processing (Computer science | Computational linguistics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Document Preparation and Text Processing | Computational Linguistics | Artificial Intelligence (incl. Robotics)DDC classification: 519 Online resources: Click here to access online
Contents:
Fundamentals -- Background on Text Classification -- Kernel Machines for Text Classification -- Approaches and techniques -- Enhancing SVMs for Text Classification -- Scaling RVMs for Text Classification -- Distributing Text Classification in Grid Environments -- Framework for Text Classification.
In: Springer eBooksSummary: 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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Fundamentals -- Background on Text Classification -- Kernel Machines for Text Classification -- Approaches and techniques -- Enhancing SVMs for Text Classification -- Scaling RVMs for Text Classification -- Distributing Text Classification in Grid Environments -- Framework for Text Classification.

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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