Intelligent Data Analysis (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
515
Utgivningsdatum
2002-04-01
Upplaga
2nd rev. and ext. ed. 2003. Corr. 2nd printing 2006
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Hand, D.J.
Illustratör/Fotograf
39 Tab 115 Abb
Illustrationer
XI, 515 p.
Dimensioner
240 x 160 x 35 mm
Vikt
1080 g
Antal komponenter
1
Komponenter
xi, 514 p. :
ISBN
9783540430605
Intelligent Data Analysis (inbunden)

Intelligent Data Analysis

An Introduction

Inbunden, Engelska, 2002-04-01
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This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.
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From the reviews of the second edition: "One excellent feature of the second addition ... . This is a particularly nice overview with excellent descriptions and numerous illustrations, most in color, for a wide variety of types of visualizations. " (E. Ziegel, Technometrics, 2005) "In this second edition ... have expanded the coverage of topics and ensured that this remains the key text for surveying the field. The twelve chapters which make up the book provide an academically rigorous and concise to the key methodologies which make up the discipline. ... In all this is a comprehensive survey of the field, and will appeal to graduate and post-graduate students, researchers and academics seeking an overview of the theoretical tools available for intelligently analyzing large, complex data sets." (TechBookReport, November, 2003)

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Innehållsförteckning

Statistical Concepts.- Statistical Methods.- Bayesian Methods.- Support Vector and Kernel Methods.- Analysis of Time Series.- Rule Induction.- Neural Networks.- Fuzzy Logic.- Stochastic Search Methods.- Visualization.- Systems and Applications.