Intelligent Data Analysis: An Introduction

✍ Scribed by David J. Hand (auth.), Michael Berthold, David J. Hand (eds.)

Publisher: Springer-Verlag Berlin Heidelberg
Year: 2003
Tongue: English
Leaves: 514
Edition: 2
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-XI
Introduction....Pages 1-15
Statistical Concepts....Pages 17-68
Statistical Methods....Pages 69-129
Bayesian Methods....Pages 131-168
Support Vector and Kernel Methods....Pages 169-197
Analysis of Time Series....Pages 199-227
Rule Induction....Pages 229-267
Neural Networks....Pages 269-320
Fuzzy Logic....Pages 321-350
Stochastic Search Methods....Pages 351-401
Visualization....Pages 403-427
Systems and Applications....Pages 429-443
Back Matter....Pages 445-514

✦ Subjects

Pattern Recognition; Statistical Theory and Methods; Information Storage and Retrieval; Artificial Intelligence (incl. Robotics); Probability and Statistics in Computer Science; Data Mining and Knowledge Discovery

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