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The author has written an accessible book presenting the central issues and placing particular emphasis on comparison, performance assessment and how to match method to application. He reviews the different approaches and guides researchers (and the huge class of users and potential users) to suitable classes of techniques. He points the way to more detailed study in "further reading" sections in each chapter.

Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical statistics --- Discriminant analysis --- Classification --- Analyse discriminante --- 519.2 --- Analysis, Discriminant --- Classification theory (Statistics) --- Discrimination theory (Statistics) --- Multivariate analysis --- Knowledge, Classification of --- Information organization --- Probability. Mathematical statistics --- Classification. --- Discriminant analysis. --- 519.2 Probability. Mathematical statistics

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Mathematical statistics --- Analyse discriminante --- 519.71 --- Analysis, Discriminant --- Classification theory (Statistics) --- Discrimination theory (Statistics) --- Control systems theory: mathematical aspects --- 519.71 Control systems theory: mathematical aspects --- Discriminant analysis --- Multivariate analysis --- Discriminant analysis. --- Discrimenant analysis

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Artificial intelligence --- Computer science

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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.

Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical statistics --- Artificial intelligence. --- Mathematical statistics. --- Data processing. --- Statistics . --- Information technology. --- Business—Data processing. --- Information storage and retrieval. --- Computational complexity. --- Artificial Intelligence. --- Statistics, general. --- IT in Business. --- Information Storage and Retrieval. --- Complexity. --- Complexity, Computational --- Electronic data processing --- Machine theory --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers