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Intelligent Data Analysis [electronic resource] : An Introduction / edited by Michael R. Berthold, David J Hand.

Contributor(s): Berthold, Michael R [editor.] | Hand, David J [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003Edition: 2nd ed. 2003.Description: XI, 515 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540486251.Subject(s): Pattern recognition systems | Statistics  | Information storage and retrieval systems | Artificial intelligence | Computer science—Mathematics | Mathematical statistics | Data mining | Automated Pattern Recognition | Statistical Theory and Methods | Information Storage and Retrieval | Artificial Intelligence | Probability and Statistics in Computer Science | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.4 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: This monograph is a detailed introductory presentation of the key classes of intelligent data analysis (IDA) methods. The 12 coherently written chapters by leading experts provide complete coverage of the core issues. The previous edition was completely revised and a new chapter on kernel methods and support vector machines and a chapter on visualization techniques were added. The revised chapters from the original edition cover classical statistics issues, ranging from the basic concepts of probability through general notions of inference to advanced multivariate and time-series methods, and provide a detailed discussion of the increasingly important Bayesian approaches. The remaining chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions to the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a higher-level overview of the IDA processes, illustrating the breadth of application of the presented ideas. The second edition features an extensive index, which makes this volume also useful as a quick reference on the key techniques in intelligent data analysis.
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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.

This monograph is a detailed introductory presentation of the key classes of intelligent data analysis (IDA) methods. The 12 coherently written chapters by leading experts provide complete coverage of the core issues. The previous edition was completely revised and a new chapter on kernel methods and support vector machines and a chapter on visualization techniques were added. The revised chapters from the original edition cover classical statistics issues, ranging from the basic concepts of probability through general notions of inference to advanced multivariate and time-series methods, and provide a detailed discussion of the increasingly important Bayesian approaches. The remaining chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions to the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a higher-level overview of the IDA processes, illustrating the breadth of application of the presented ideas. The second edition features an extensive index, which makes this volume also useful as a quick reference on the key techniques in intelligent data analysis.

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