000			06485cam a2200277 i 4500
999			_c35250 _d35250
003			CUTN
005			20210806151226.0
008			180530m20199999njua b 001 0 eng
020			_a9789813271227 (hc : alk. paper : v. 1)
020			_a9813271221 (hc : alk. paper : v. 1)
041			_aEnglish
042			_apcc
082	0	0	_a006.31 _223 _bMAR
100	1		_aMarwala, Tshilidzi,
245	1	0	_aHandbook of machine learning _cTshilidzi Marwala (University of Johannesburg, South Africa).
300			_avolumes : _billustrations ; _c25 cm
505	0		_avolume 1. Foundation of artificial intelligence -- _tContents 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
520			_aThis 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.
650		0	_aMachine learning.
650		0	_aArtificial intelligence. _94
942			_2ddc _cRB
100	1		_d1971- _eauthor.
504			_aIncludes bibliographical references and index.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg