Text Mining with MATLAB®

Rafael E. Banchs

ISBN 10: 1461441501 / ISBN 13: 9781461441502

Verlag: Springer New York, Springer US, 2012

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - Text Mining with MATLAB provides a comprehensive introduction to text mining using MATLAB. It's designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications. The first part provides an introduction to basic procedures for handling and operating with text strings. Then, it reviews major mathematical modeling approaches. Statistical and geometrical models are also described along with main dimensionality reduction methods. Finally, it presents some specific applications such as document clustering, classification, search and terminology extraction.All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.

Bestandsnummer des Verkäufers 9781461441502

Bibliografische Details

• Titel: Text Mining with MATLAB®
• Verlag: Springer New York, Springer US
• Erscheinungsjahr: 2012
• Sprache: Englisch
• ISBN-10: 1461441501
• ISBN-13: 9781461441502
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 26 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 26 mm
• Gewicht: 717 Gramm

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Inhaltsangabe

Text Mining with MATLAB provides a comprehensive introduction to text mining using MATLAB. It's designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications.

The first part provides an introduction to basic procedures for handling and operating with text strings. Then, it reviews major mathematical modeling approaches. Statistical and geometrical models are also described along with main dimensionality reduction methods. Finally, it presents some specific applications such as document clustering, classification, search and terminology extraction.

All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.

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