Intelligent Data Analysis

by ;
Edition: 2nd
Format: Hardcover
Pub. Date: 2003-05-01
Publisher(s): Springer Nature
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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.

Table of Contents

Introduction
1(16)
Why ``Intelligent Data Analysis''?
1(3)
How the Computer Is Changing Things
4(4)
The Nature of Data
8(4)
Modern Data Analytic Tools
12(2)
Conclusion
14(3)
Statistical Concepts
17(52)
Introduction
17(1)
Probability
18(11)
Sampling and Sampling Distributions
29(4)
Statistical Inference
33(13)
Prediction and Prediction Error
46(11)
Resampling
57(11)
Conclusion
68(1)
Statistical Methods
69(62)
Introduction
69(1)
Generalized Linear Models
70(23)
Special Topics in Regression Modelling
93(7)
Classical Multivariate Analysis
100(29)
Conclusion
129(2)
Bayesian Methods
131(38)
Introduction
131(1)
The Bayesian Paradigm
132(3)
Bayesian Inference
135(8)
Bayesian Modeling
143(10)
Bayesian Networks
153(14)
Conclusion
167(2)
Support Vector and Kernel Methods
169(30)
Example: Kernel Perceptron
170(6)
Overfitting and Generalization Bounds
176(5)
Support Vector Machines
181(13)
Kernel PCA and CCA
194(2)
Conclusion
196(3)
Analysis of Time Series
199(30)
Introduction
199(3)
Linear Systems Analysis
202(5)
Nonlinear Dynamics Basics
207(6)
Delay-Coordinate Embedding
213(5)
Examples
218(8)
Conclusion
226(3)
Rule Induction
229(40)
Introduction
229(3)
Propositional rule learning
232(4)
Rule learning as search
236(6)
Evaluating the quality of rules
242(4)
Propositional rule induction at work
246(4)
Learning first-order rules
250(12)
Some ILP systems at work
262(5)
Conclusion
267(2)
Neural Networks
269(52)
Introduction
269(1)
Fundamentals
270(8)
Multilayer Feedforward Neural Networks
278(5)
Learning and Generalization
283(9)
Radial Basis Function Networks
292(8)
Competitive Learning
300(7)
Principal Components Analysis and Neural Networks
307(5)
Time Series Analysis
312(7)
Conclusion
319(2)
Fuzzy Logic
321(30)
Introduction
321(1)
Basics of Fuzzy Sets and Fuzzy Logic
322(14)
Extracting Fuzzy Models from Data
336(10)
Fuzzy Decision Trees
346(4)
Conclusion
350(1)
Stochastic Search Methods
351(52)
Introduction
351(3)
Stochastic Search by Simulated Annealing
354(6)
Stochastic, Adaptive Search by Evolution
360(2)
Evolution Strategies
362(12)
Genetic Algorithms
374(16)
Genetic Programming
390(10)
Conclusion
400(3)
Visualization
403(26)
Introduction
403(2)
Classification of Visual Data Analysis Techniques
405(1)
Data Type to be Visualized
406(5)
Visualization Techniques
411(3)
Interaction Techniques
414(4)
Specific Visual Data Analysis Techniques
418(8)
Conclusion
426(3)
Systems and Applications
429(16)
Introduction
429(1)
Diversity of IDA Applications
430(6)
Several Development Issues
436(6)
Conclusion
442(3)
Appendix A: Tools
445(20)
A.1 Tools for statistical analysis
445(2)
A.2 Tools for exploration/modeling
447(7)
A.3 Tools for Text and Web Mining
454(2)
A.4 Data Analysis Suites
456(8)
A.5 Conclusion
464(1)
Appendix B: Information-Theoretic Tree and Rule Induction
465(10)
B.1 Information and Uncertainty
465(3)
B.2 Decision Tree Induction
468(2)
B.3 Rule Induction
470(5)
References 475(26)
Index 501(12)
Author Addresses 513

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