Intelligent Data Analysis : An Introduction

by ; ; ;
Edition: 1st
Format: Hardcover
Pub. Date: 1999-09-01
Publisher(s): Springer-Verlag New York Inc
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Summary

This monograph is a detailed introductory 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.

Table of Contents

Introduction
1(14)
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(1)
Statistical Concepts
15(52)
Introduction
15(1)
Probability
16(11)
Sampling and Sampling Distributions
27(4)
Statistical Inference
31(13)
Prediction and Prediction Error
44(11)
Resampling
55(11)
Conclusion
66(1)
Statistical Methods
67(62)
Introduction
67(1)
Generalized Linear Models
68(24)
Special Topics in Regression Modelling
92(7)
Classical Multivariate Analysis
99(28)
Conclusion
127(2)
Bayesian Methods
129(38)
Introduction
129(1)
The Bayesian Paradigm
130(3)
Bayesian Inference
133(7)
Bayesian Modeling
140(11)
Bayesian Networks
151(14)
Conclusion
165(2)
Analysis of Time Series
167(28)
Introduction
167(2)
Linear Systems Analysis
169(5)
Nonlinear Dynamics Basics
174(6)
Delay-Coordinate Embedding
180(5)
Examples
185(8)
Conclusion
193(2)
Rule Induction
195(22)
Introduction
195(1)
Main Problem: Data Fit vs. Mental Fit
196(3)
Elements of Rule Induction
199(7)
Information-Theoretic Approach
206(9)
Conclusion
215(2)
Neural Networks
217(52)
Introduction
217(1)
Fundamentals
218(8)
Multilayer Feedforward Neural Networks
226(5)
Learning and Generalization
231(10)
Radial Basis Function Networks
241(7)
Competitive Learning
248(7)
Principlal Components Analysis and Neural Networks
255(5)
Time Series Analysis
260(8)
Conclusion
268(1)
Fuzzy Logic
269(30)
Introduction
269(1)
Basics of Fuzzy Sets and Fuzzy Logic
270(14)
Extracting Fuzzy Models from Data
284(9)
Fuzzy Decision Trees
293(4)
Conclusion
297(2)
Stochastic Search Methods
299(52)
Introduction
299(3)
Stochastic Search by Simulated Annealing
302(6)
Stochastic, Adaptive Search by Evolution
308(2)
Evolution Strategies
310(13)
Genetic Algorithms
323(13)
Genetic Programming
336(12)
Conclusion
348(3)
Systems and Applications
351(14)
Introduction
351(1)
Diversity and IDA Applications
352(5)
Several Development Issues
357(6)
Conclusion
363(2)
Appendix A: Tools 365(10)
A.1 Tools for Statistical Analysis
365(1)
A.2 Tools for Exploration / Modeling
366(5)
A.3 Data Analysis Suites
371(2)
A.4 Conclusions and Future Perspective
373(2)
References 375(20)
Index 395(4)
Author Information 399

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