# Handbook of machine learning Tshilidzi Marwala (University of Johannesburg, South Africa).

Material type: TextLanguage: English Description: volumes : illustrations ; 25 cmISBN:- 9789813271227 (hc : alk. paper : v. 1)
- 9813271221 (hc : alk. paper : v. 1)

- 006.31 23 MAR

Item type | Current library | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|

Reference Books | CUTN Central Library Reference | Non-fiction | 006.31 MAR (Browse shelf(Opens below)) | Not for loan | 44010 |

volume 1. Foundation of artificial intelligence -- 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 Counter factual 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

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.

Includes bibliographical references and index.

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