Intelligent data analysis : An introduction
By: Berthold, Michael [Editor]
.
Contributor(s): Hand, David [Editor].
Publisher: Berlin Springer 2007Edition: 2nd.Description: xi, 514p. | Binding- Hard Bound | 24*16.2 cm.ISBN: 9783662578834.Subject(s): Computer EngineeringDDC classification: 519.5 Summary: This monograph is a detailed introcuctory presentation of the key classes of intelligent data analysis methods. The ten 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, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approach. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into 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 process and illustrations of the breadth of application of the ideas. The book will become a valuable source of reference for professionals concerned with modern data analysis. Students as well as IT professionals interested in learning about intelligent data analysis will appreciate the book as useful text enhanced by numerous illustrations and examples.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Text Books
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School of Engineering & Technology Reference Section | Reference | 519.5 BER/HAN (Browse shelf) | Available | E15172 |
This monograph is a detailed introcuctory presentation of the key classes of intelligent data analysis methods. The ten 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, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approach. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into 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 process and illustrations of the breadth of application of the ideas. The book will become a valuable source of reference for professionals concerned with modern data analysis. Students as well as IT professionals interested in learning about intelligent data analysis will appreciate the book as useful text enhanced by numerous illustrations and examples.
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