Intelligent data analysis : an introduction /
Intelligent data analysis : an introduction /
editors, Michael Berthold, David J. Hand.
- 2nd rev. and extended ed.
- Berlin ; New York : Springer, 2003.
- xi, 514 p. 24 cm.
1. Introduction
1.1 Why "Iiit('lli{i;(>iit Data Analysis"?
1.2 How tiic Computer Is Cliangiii{^ Tilings
1.3 The Nature of Data
1.4 Modern Data .
alytie Tools
1.5 Conclusion
2. Statistical Concepts
2.1 Introduction
2.2 Prohahility
2.3 Sampling and .Sampling Distributions . .
2.4 Statistical Inference
2.5 Prediction and Prediction Error.
2.G Ilesam])ling
2.7 Conclusion
3. Statistical Methods
3.1 Introdnct ion
.3.2 Cenerali/ed Linear Models
3.3 Sjiecitd Tojiics in Regrc^.ssion Modelling.
3.4 Chi.ssical .Multivariate .Vnalysis
3.5 Conclusion
4. Bayesian Methods
4.1 Int rodnct ion
4.2 The Hayesian Paradigm ....
4.3 Bayesian Inference
4.4 Bayesian Modeling
4.5 Bayesian Networks
4.G Conclusion
5. Support Vector and Kernel Methods
5.1 l'..\anii)le: Kernel Perce|)tron
5.2 Overiitting and (Jeneialization Bounds. .
5.3 Support N'ector .Machines
•"). 1 K(Mii('i PCA ;iii(l CCA
■').') Conclusion
6. Analysis of Time Series. .
. 1 Intioductioii
G.2 Liiicnr Systems .
alysis
G..3 .Xoniiiiear Dynamics Basics . . .
G.4 Delay-Cooidinate Embedding.
G.-j Examples
G.G Conclusion
7. Rule Induction
7.1 Intfoduction
7.2 Proposirional rul(> learning
7.3 Rule learning as .search
7.4 Evalnating the (inality of rules
7.3 Propositiotial rule induction at work. .
7.G Learnitig fi rst-order rnles
7.7 Some ILP systems at work
7.8 Conchisioti
8. Neural Networks
8.1 Introduction
8.2 FmidanKmtals
8.3 .Multilayer F(>edforward Neural Networks .
8.4 Learning and Generali/.ttt ion
8.0 Radial Basis Fnnction .N(
8.7 Principal Cotnponenis .Analysis and Neural .Networks
8.8 Titne Series .Analysis
8.9 Conclusion
9. Fuzzy Logic
9.1 Introduct ion
9.2 Basics of Fti/./y S(>ts and Fu/./.y Logic
9.3 F.xtracting Fnz/y Mixlels from Data .
9.4 Fu/,/.y Decision Trees
9.5 Conclusion
10. Stochastic Search Methods
10.1 Ititroductioii • • ■
10.2 .Stochastic Search by Simulated .Antiealing.
10.3 .Stochastic. .Adaptive .Setirch by Evolution .
10.4 Evohtt ioti Strategies
10.5 (hnetic
10.C) Genetic ProRTaniniing .
10.7 Goiielusion
11. Visualization
11.1 lutroduetion
11.2 Cla.ssifieatioii of \'isual Data .Analysis Techniques
11.3 Data Type to he Msualized
11.4 \'isuali/.ation Tecliniciues
11.5 Interaction Techni
11.7 Conclusion
12. Systems and Applications
12.1 Introduction
12.2 Diversity of IDA .Ajiplications .
12.3 Several Develojjinent Issues. . . .
12.4 Conclusion
9783540430605)
Mathematical Statistics.
Mathematical Statistics--Data processing.
Artificial Intelligence.
001.433 / BER/I
1. Introduction
1.1 Why "Iiit('lli{i;(>iit Data Analysis"?
1.2 How tiic Computer Is Cliangiii{^ Tilings
1.3 The Nature of Data
1.4 Modern Data .
alytie Tools
1.5 Conclusion
2. Statistical Concepts
2.1 Introduction
2.2 Prohahility
2.3 Sampling and .Sampling Distributions . .
2.4 Statistical Inference
2.5 Prediction and Prediction Error.
2.G Ilesam])ling
2.7 Conclusion
3. Statistical Methods
3.1 Introdnct ion
.3.2 Cenerali/ed Linear Models
3.3 Sjiecitd Tojiics in Regrc^.ssion Modelling.
3.4 Chi.ssical .Multivariate .Vnalysis
3.5 Conclusion
4. Bayesian Methods
4.1 Int rodnct ion
4.2 The Hayesian Paradigm ....
4.3 Bayesian Inference
4.4 Bayesian Modeling
4.5 Bayesian Networks
4.G Conclusion
5. Support Vector and Kernel Methods
5.1 l'..\anii)le: Kernel Perce|)tron
5.2 Overiitting and (Jeneialization Bounds. .
5.3 Support N'ector .Machines
•"). 1 K(Mii('i PCA ;iii(l CCA
■').') Conclusion
6. Analysis of Time Series. .
. 1 Intioductioii
G.2 Liiicnr Systems .
alysis
G..3 .Xoniiiiear Dynamics Basics . . .
G.4 Delay-Cooidinate Embedding.
G.-j Examples
G.G Conclusion
7. Rule Induction
7.1 Intfoduction
7.2 Proposirional rul(> learning
7.3 Rule learning as .search
7.4 Evalnating the (inality of rules
7.3 Propositiotial rule induction at work. .
7.G Learnitig fi rst-order rnles
7.7 Some ILP systems at work
7.8 Conchisioti
8. Neural Networks
8.1 Introduction
8.2 FmidanKmtals
8.3 .Multilayer F(>edforward Neural Networks .
8.4 Learning and Generali/.ttt ion
8.0 Radial Basis Fnnction .N(
8.7 Principal Cotnponenis .Analysis and Neural .Networks
8.8 Titne Series .Analysis
8.9 Conclusion
9. Fuzzy Logic
9.1 Introduct ion
9.2 Basics of Fti/./y S(>ts and Fu/./.y Logic
9.3 F.xtracting Fnz/y Mixlels from Data .
9.4 Fu/,/.y Decision Trees
9.5 Conclusion
10. Stochastic Search Methods
10.1 Ititroductioii • • ■
10.2 .Stochastic Search by Simulated .Antiealing.
10.3 .Stochastic. .Adaptive .Setirch by Evolution .
10.4 Evohtt ioti Strategies
10.5 (hnetic
10.C) Genetic ProRTaniniing .
10.7 Goiielusion
11. Visualization
11.1 lutroduetion
11.2 Cla.ssifieatioii of \'isual Data .Analysis Techniques
11.3 Data Type to he Msualized
11.4 \'isuali/.ation Tecliniciues
11.5 Interaction Techni
11.7 Conclusion
12. Systems and Applications
12.1 Introduction
12.2 Diversity of IDA .Ajiplications .
12.3 Several Develojjinent Issues. . . .
12.4 Conclusion
9783540430605)
Mathematical Statistics.
Mathematical Statistics--Data processing.
Artificial Intelligence.
001.433 / BER/I