Handbook of Machine Learning, Volume 2: Optimization And Decision Making
- Length: 322 pages
- Edition: 1
- Language: English
- Publisher: WSPC
- Publication Date: 2019-11-11
- ISBN-10: 9811205663
- ISBN-13: 9789811205668
- Sales Rank: #9836435 (See Top 100 Books)
Building on Handbook of Machine Learning – Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.
Cover Page Title Page Copyright Page Preface About the Authors Acknowledgments Contents Chapter 1: Introduction 1.1 Introduction 1.2 A Brief Description of This Book 1.3 Conclusion References Chapter 2: Classical Optimization 2.1 Introduction 2.2 Optimization 2.3 Nelder–Mead Simplex Method 2.4 Quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) Algorithm 2.5 Conjugate Gradient (CG) Method 2.6 Parameter Estimation of a Simple Beam 2.7 Parameter Estimation of an Unsymmetrical H-shaped Structure 2.8 Conclusion References Chapter 3: Genetic Algorithm 3.1 Genetic Algorithm 3.1.1 Initialization 3.1.2 Crossover 3.1.3 Mutation 3.1.4 Selection 3.1.5 Termination 3.2 Remanufacturing 3.3 Unsymmetrical H-shaped Structure 3.4 Conclusion References Chapter 4: Particle Swarm Optimization 4.1 Introduction 4.2 Particle Swarm Optimization 4.3 Genetic Algorithm Versus PSO 4.4 Operating System Scheduler Tuning 4.5 Unsymmetrical H-shaped Structure 4.6 Missing Data Estimation 4.7 Conclusion References Chapter 5: Simulated Annealing 5.1 Introduction 5.2 Simulated Annealing (SA) 5.2.1 Simulated annealing parameters 5.2.2 Transition probabilities 5.2.3 Monte Carlo method 5.2.4 Markov Chain Monte Carlo (MCMC) 5.2.5 Acceptance probability function: Metropolis algorithm 5.2.6 Cooling schedule 5.3 Particle Swarm Optimization Versus SA 5.4 Solving Sudoku 5.5 Unsymmetrical H-shaped Structure 5.6 Conclusion References Chapter 6: Response Surface Method 6.1 Introduction 6.2 Response Surface Method (RSM) 6.3 Neural Networks 6.3.1 Multi-layer perceptron (MLP) 6.3.2 Training the multi-layer perceptron 6.3.3 Backpropagation method 6.3.4 Scaled conjugate gradient method 6.4 Evolutionary Optimization 6.5 Estimating Parameters of a Beam 6.6 Estimating Parameters of an Unsymmetrical H-shaped Structure 6.7 Conclusion References Chapter 7: Ant Colony Optimization 7.1 Introduction 7.2 Ant Colony Optimization 7.3 Missing Data Estimation Using ACO and Neural Networks 7.4 Condition Monitoring of Bushings 7.4.1 Dissolved gas analysis (DGA) experiment 7.4.2 Data preprocessing 7.5 Conclusion References Chapter 8: Bat and Firefly Algorithms 8.1 Introduction 8.2 Bat Algorithm 8.3 Firefly Algorithm 8.4 Deep Learning 8.5 Restricted Boltzmann Machine (RBM) 8.6 Contrastive Divergence (CD) 8.7 Missing Data Estimation Using BA, FA and Neural Networks 8.8 Conclusion References Chapter 9: Artificial Immune System 9.1 Introduction 9.2 Artificial Immune System 9.2.1 Innate immunity 9.2.2 Acquired immunity 9.2.3 Self-tolerant trait 9.2.4 Immune system memory 9.3 Artificial Immune System Basic Concepts 9.3.1 Initialization/Encoding 9.3.2 Similarity or affinity measure 9.3.3 Somatic hypermutation 9.4 The Concept of the Negative Selection Algorithm 9.4.1 Implementation of algorithm 9.4.2 Training 9.4.3 Testing 9.5 Missing Data Estimation Using AIS and Neural Networks 9.6 Conclusion References Chapter 10: Invasive Weed Optimization and Cuckoo Search Algorithms 10.1 Introduction 10.2 Invasive Weed Optimization 10.3 Cuckoo Search Algorithm 10.4 Deep Autoassociative Neural Network 10.5 Missing Data Estimation Using IWO, CS and Neural Networks 10.6 Conclusion References Chapter 11: Decision Trees and Random Forests 11.1 Introduction 11.2 Decision Trees 11.3 Random Forests 11.3.1 Common variables for random forest 11.4 Missing Data Estimation Using Decision Trees and AANN-GA 11.5 Missing Data Estimation Using Random Forests 11.5.1 Missing data estimation using random forests hybrids 11.6 Conclusion References Chapter 12: Hybrid Methods 12.1 Introduction 12.2 Finite Element Model Updating 12.3 Nelder–Mead Simplex Method 12.4 Structural Mechanics 12.5 Hybrid PSO and the Nelder–Mead Simplex 12.6 Finite Element Updating 12.7 Conclusion References Chapter 13: Economic Modeling 13.1 Introduction 13.2 Economic Concepts 13.3 Pitfalls on Economic Data Modeling 13.4 Data Handling 13.5 Neural Networks 13.6 Support Vector Machine 13.7 Rough Sets 13.8 Autoassociative Networks 13.9 Incremental Learning 13.10 Conclusion References Chapter 14: Condition Monitoring 14.1 Introduction 14.2 Condition Monitoring Framework 14.3 Stages of Condition Monitoring 14.4 Data Representation for Condition Monitoring 14.4.1 Time domain 14.4.2 Modal domain 14.4.3 Frequency domain 14.4.4 Time–frequency 14.5 Correlation-Based Condition Monitoring 14.6 Finite Element-Based Condition Monitoring 14.7 Artificial Intelligence Methods 14.7.1 Multi-layer perceptrons 14.7.2 Hidden Markov models (HMMs) 14.7.3 Fuzzy logic 14.7.4 Rough sets 14.7.5 Support vector machines (SVMs) 14.8 Conclusion References Chapter 15: Rational Decision-Making 15.1 Introduction 15.2 Can Rationality be Measured? 15.2.1 Information 15.2.2 Model: Biological and physical brain 15.2.3 What about the uncertainty principle? 15.2.4 Optimization 15.2.5 Classification of rationality 15.2.6 Rationality quantification 15.3 Can AI Machines Be Rational? 15.3.1 What did Herbert Simon say? 15.3.2 What happens when we replace humans with machines? 15.3.3 Digital and quantum computing 15.3.4 All models are wrong 15.3.5 Can machines be rational? 15.4 Are Markets Rational? 15.5 Unbounded Rational Decision-Making 15.6 Bounded Rational Decision-Making 15.7 Flexibly Bounded Rational Decision-Making 15.8 Condition Monitoring 15.9 Modeling HIV 15.10 Conclusion References Chapter 16: Concluding Remarks 16.1 Summary of the Book References Index
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