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This monograph is a detailed introductory presentation of 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, 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 approaches and Support Vector Machines. 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 chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.
List of 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.
About the author
David J. Hand, Professor em. Für Mathematik und Senior Research Investigator am Imperial College London, ist ehemaliger Präsident der Royal Statistical Society und derzeit wissenschaftlicher Chefberater von Winston Capital Management, einem der erfolgreichsten auf automatisierten (algorithmischen) Handel spezialisierten Hedgefonds in Europa. David Hand lebt in London.
Summary
This monograph is a detailed introductory presentation of 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, 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 approaches and Support Vector Machines. 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 chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.
Additional text
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)
Report
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)
