Foundation of artificial intelligence

✍ Scribed by Tshilidzi Marwala

• Publisher: World Scientific Publishing Co
• Year: 2019
• Tongue: English
• Leaves: 329
• Series: Handbook of machine learning
• Category: Library

✦ Synopsis

This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.

Readership: Practitioners in artificial intelligence.

✦ Table of Contents

Contents
Preface
About the Author
Acknowledgements
1. Introduction
1.1 Introduction
1.2 Time Domain Data
1.2.1 Average
1.2.2 Variance
1.2.3 Kurtosis
1.3 Frequency Domain
1.4 Time–Frequency Domain
1.5 Fractals
1.6 Stationarity
1.7 Common Mistakes on Handling Data
1.8 Outline of the Book
1.9 Conclusions
References
2. Multi-layer Perceptron
2.1 Introduction
2.2 Multi-layer Perceptron
2.3 Training the Multi-layered Perceptron
2.4 Back-propagation Method
2.5 Scaled Conjugate Method
2.6 Multi-layer Perceptron Classifier
2.7 Applications to Economic Modelling
2.8 Application to a Steam Generator
2.9 Application to Cylindrical Shells
2.10 Application to Interstate Conflict
2.11 Conclusions
References
3. Radial Basis Function
3.1 Introduction
3.2 Radial Basis Function
3.3 Model Selection
3.4 Application to Interstate Conflict
3.5 Call Behaviour Classification
3.6 Modelling the CPI
3.7 Modelling Steam Generator
3.8 Conclusions
References
4. Automatic Relevance Determination
4.1 Introduction
4.2 Mathematical Basis of the Automatic Relevance Determination
4.2.1 Neural networks
4.2.2 Bayesian framework
4.2.3 Automatic relevance determination
4.3 Application to Interstate Conflict
4.4 Applications of ARD in Inflation Modelling
4.5 Conclusions
References
5. Bayesian Networks
5.1 Introduction
5.2 Neural Networks
5.3 Hybrid Monte Carlo
5.4 Shadow Hybrid Monte Carlo (SHMC) Method
5.5 Separable Shadow Hybrid Monte Carlo
5.6 Comparison of Sampling Methods
5.7 Interstate Conflict
5.8 Conclusions
References
6. Support Vector Machines
6.1 Introduction
6.2 Support Vector Machines for Classification
6.3 Support Vector Regression
6.4 Conflict Modelling
6.5 Steam Generator
6.6 Conclusions
References
7. Fuzzy Logic
7.1 Introduction
7.2 Fuzzy Logic Theory
7.3 Neuro-fuzzy Models
7.4 Steam Generator
7.5 Interstate Conflict
7.6 Conclusions
References
8. Rough Sets
8.1 Introduction
8.2 Rough Sets
8.2.1 Information system
8.2.2 The indiscernibility relation
8.2.3 Information table and data representation
8.2.4 Decision rules induction
8.2.5 The lower and upper approximation of sets
8.2.6 Set approximation
8.2.7 The reduct
8.2.8 Boundary region
8.2.9 Rough membership functions
8.3 Discretization Methods
8.3.1 Equal-width-bin (EWB) partitioning
8.3.2 Equal-frequency-bin (EFB) partitioning
8.4 Rough Set Formulation
8.5 Rough Sets vs. Fuzzy Sets
8.6 Multi-layer Perceptron Model
8.7 Neuro-rough Model
8.7.1 Bayesian training on rough sets
8.7.2 Markov Chain Monte Carlo (MCMC)
8.8 Modelling of HIV
8.9 Application to Modelling the Stock Market
8.10 Interstate Conflict
8.11 Conclusions
References
9. Hybrid Machines
9.1 Introduction
9.2 Hybrid Machine
9.2.1 Bayes optimal classifier
9.2.2 Bayesian model averaging
9.2.3 Bagging
9.2.4 Boosting
9.2.5 Stacking
9.2.6 Evolutionary machines
9.3 Theory of Hybrid Networks
9.3.1 Equal weights
9.3.2 Variable weights
9.4 Condition Monitoring
9.5 Caller Behaviour
9.6 Conclusions
References
10. Auto-associative Networks
10.1 Introduction
10.2 Auto-associative Networks
10.3 Principal Component Analysis
10.4 Missing Data Estimation
10.5 Genetic Algorithm (GA)
10.6 Machine Learning
10.7 Modelling HIV
10.8 Artificial Beer Taster
10.9 Conclusions
References
11. Evolving Networks
11.1 Introduction
11.2 Machine Learning
11.3 Genetic Algorithm
11.4 Learn++ Method
11.5 Incremental Learning Method Using Genetic Algorithm (ILUGA)
11.6 Optical Character Recognition (OCR)
11.7 Wine Recognition
11.8 Financial Analysis
11.9 Condition Monitoring of Transformers
11.10 Conclusions
References
12. Causality
12.1 Introduction
12.2 Correlation
12.3 Causality
12.4 Theories of Causality
12.4.1 Transmission theory of causality
12.4.2 Probability theory of causality
12.4.3 Projectile theory of causality
12.4.4 Causal calculus and structural learning
12.4.5 Granger causality
12.4.6 Structural learning
12.4.7 Manipulation theory
12.4.8 Process theory
12.4.9 Counterfactual theory
12.4.10 Neyman–Rubin causal model
12.4.11 Causal calculus
12.4.12 Inductive causation (IC)
12.5 How to Detect Causation?
12.6 Causality and Artificial Intelligence
12.7 Causality and Rational Decision
12.8 Conclusions
References
13. Gaussian Mixture Models
13.1 Introduction
13.2 Gaussian Mixture Models
13.3 EM Algorithm
13.4 Condition Monitoring: Transformer Bushings
13.5 Condition Monitoring: Cylindrical Shells
13.6 Condition Monitoring: Bearings
13.7 Conclusions
References
14. Hidden Markov Models
14.1 Introduction
14.2 Hidden Markov Models
14.3 Condition Monitoring: Motor Bearing Faults
14.4 Speaker Recognition
14.5 Conclusions
References
15. Reinforcement Learning
15.1 Introduction
15.2 Reinforcement Learning: TD-Lambda
15.3 Game Theory
15.4 Multi-agent Systems
15.5 Modelling the Game of Lerpa
15.6 Modelling of Tic–Tac–Toe
15.7 Conclusions
References
16. Conclusion Remarks
16.1 Summary of the Book
16.2 Implications of Artificial Intelligence
References
Index