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Inductive Inference for Large Scale Text Classification : Kernel Approaches and Techniques - Bernadete Ribeiro; Catarina Silva
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Bernadete Ribeiro; Catarina Silva:
Inductive Inference for Large Scale Text Classification : Kernel Approaches and Techniques - gebrauchtes Buch

ISBN: 3642045324

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The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence - quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automate, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results. 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 page to scientific documents, including emails, news and books. Despite the widespread use of digital texts, handing 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 kerned approaches for inductive inference in large scale text cla computers,computers and technology Computers & Technology, Springer

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Inductive Inference for Large Scale Text Classification
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2009, ISBN: 9783642045325

ID: 9783642045325

Inductive Inference for Large Scale Text Classification: Hardback: Springer-Verlag Berlin and Heidelberg GmbH & Co. K: 9783642045325: 11 Nov 2009: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks. 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. Artificial Intelligence, , , , , , , , , , , ,, [PU: Springer, Berlin/Heidelberg/New York, NY]

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Inductive Inference for Large Scale Text Classification - Catarina Silva
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Catarina Silva:
Inductive Inference for Large Scale Text Classification - neues Buch

ISBN: 9783642045325

[ED: Buch], [PU: Springer-Verlag GmbH], Neuware - 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., [SC: 0.00], Neuware, gewerbliches Angebot, FixedPrice, [GW: 411g]

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Inductive Inference for Large Scale Text Classification - Catarina Silva; Bernadete Ribeiro
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Inductive Inference for Large Scale Text Classification - gebunden oder broschiert

2009, ISBN: 9783642045325

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Kernel Approaches and Techniques, 2010, Hardcover, Buch, [PU: Springer Berlin]

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Inductive Inference for Large Scale Text Classification - Catarina Silva
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Catarina Silva:
Inductive Inference for Large Scale Text Classification - neues Buch

ISBN: 9783642045325

ID: 92532396_nw

Livre, [PU: Springer, Berlin/Heidelberg/New York, NY]

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Details zum Buch
Inductive Inference for Large Scale 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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EAN (ISBN-13): 9783642045325
ISBN (ISBN-10): 3642045324
Gebundene Ausgabe
Erscheinungsjahr: 2009
Herausgeber: Springer-Verlag GmbH
155 Seiten
Gewicht: 0,411 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 17.07.2009 13:33:09
Buch zuletzt gefunden am 24.10.2017 13:55:01
ISBN/EAN: 9783642045325

ISBN - alternative Schreibweisen:
3-642-04532-4, 978-3-642-04532-5


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