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Intelligent Data Analysis
Synopses & Reviews
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.
Includes bibliographical references (p. -514) and index.
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.
Table of Contents
1. Introduction; 2. Statistical Concepts; 3. Statistical Methods; 4. Bayesian Methods; 5. Support Vector and Kernel Methods; 6. Analysis of Time Series; 7. Rule Induction; 8. Neural Networks; 9. Fuzzy Logic; 10. Stochastic Search Methods; 11 Visualization; 12. Systems and Applications; Appendix A: Information Theory and Decision Tree Induction; Appendix B: Tools; References; Index; Author Addresses.
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