Intelligent Data Analysis: An Introduction

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Springer Science & Business Media, 2003 - Computers - 514 pages
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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.

 

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Contents

Introduction
1
12 How the Computer Is Changing Thingsthe Merger of Disciplines
4
13 The Nature of Data
8
14 Modern Data Analytic Tools
12
15 Conclusion
14
Statistical Concepts
16
22 Probability
18
23 Sampling and Sampling Distributions
29
82 Fundamentals
270
83 Multilayer Feedforward Neural Networks
278
84 Learning and Generalization
283
85 Radial Basis Function Networks
292
86 Competitive Learning
300
87 Principal Components Analysis and Neural Networks
307
88 Time Series Analysis
312
89 Conclusion
319

24 Statistical Inference
33
25 Prediction and Prediction Error
46
26 Resampling
57
27 Conclusion
68
Statistical Methods
69
32 Generalized Linear Models
70
33 Special Topics in Regression Modelling
93
34 Classical Multivariate Analysis
100
35 Conclusion
129
Bayesian Methods
131
42 The Bayesian Paradigm
132
43 Bayesian Inference
135
44 Bayesian Modeling
143
45 Bayesian Networks
153
46 Conclusion
167
Support Vector and Kernel Methods
169
Kernel Perceptron
170
52 Overfitting and Generalization Bounds
176
53 Support Vector Machines
181
54 Kernel PCA and CCA
194
55 Conclusion
196
Analysis of Time Series
198
62 Linear Systems Analysis
202
63 Nonlinear Dynamics Basics
207
64 DelayCoordinate Embedding
213
65 Examples
218
66 Conclusion
226
Rule Induction
229
72 Propositional rule learning
232
73 Rule learning as search
236
74 Evaluating the quality of rules
242
75 Propositional rule induction at work
246
76 Learning firstorder rules
250
77 Some ILP systems at work
262
78 Conclusion
267
Neural Networks
268
Fuzzy Logic
321
92 Basics of Fuzzy Sets and Fuzzy Logic
322
93 Extracting Fuzzy Models from Data
336
94 Fuzzy Decision Trees
346
95 Conclusion
350
Stochastic Search Methods
351
102 Stochastic Search by Simulated Annealing
354
103 Stochastic Adaptive Search by Evolution
360
104 Evolution Strategies
362
105 Genetic Algorithms
374
106 Genetic Programming
390
107 Conclusion and Summary
400
Visualization
403
112 Classification of Visual Data Analysis Techniques
405
113 Data Type to be Visualized
406
114 Visualization Techniques
411
115 Interaction Techniques
414
116 Specific Visual Data Analysis Techniques
418
117 Conclusion
426
Systems and Applications
428
122 Diversity of IDA Applications
430
123 Several Development Issues
436
124 Conclusion
442
Tools
445
A2 Tools for explorationmodeling
447
A3 Tools for Text and Web Mining
454
A4 Data Analysis Suites
456
A5 Conclusion
464
InformationTheoretic Tree and Rule Induction
465
B2 Decision Tree Induction
468
B3 Rule Induction
470
References
474
Index
501
Author Addresses
513
Copyright

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