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• Introduction.- Decoding Choosing.- ML of Choosing: Architecting Intelligent Choice Framework.- Machine Learning of Choice Economics.- Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way.- Choice Architecture - Machine Learning Framework.- Artificial Consciousness and Choosing (Towards Conscious Choice Machines).- Choice Computing and Creativity.- Experimental Choice Computing and Choice Learning Through Real-Life Stories.- Beyond Choice Computing.
• (source: Nielsen Book Data)

This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects - one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products - help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.
(source: Nielsen Book Data)

• ch. 1: Introduction to Reinforcement and Systemic Machine Learning
• 1.1. Introduction
• 1.2. Supervised, Unsupervised, and Semisupervised Machine Learning
• 1.3. Traditional Learning Methods and History of Machine Learning
• 1.4. What is Machine Learning?
• 1.5. Machine-Learning Problem
• 1.6. Learning Paradigms
• 1.7. Machine-Learning Techniques and Paradigms
• 1.8. What is Reinforcement Learning?
• 1.9. Reinforcement Function and Environment Function
• 1.10. Need of Reinforcement Learning
• 1.11. Reinforcement Learning and Machine Intelligence
• 1.12. What is Systemic Learning?
• 1.13. What Is Systemic Machine Learning?
• 1.14. Challenges in Systemic Machine Learning
• 1.15. Reinforcement Machine Learning and Systemic Machine Learning
• 1.16. Case Study Problem Detection in a Vehicle
• 1.17. Summary
• Reference.

• ch. 2: Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning
• 2.1. Introduction
• 2.2. What is Systemic Machine Learning?
• 2.3. Generalized Systemic Machine-Learning Framework
• 2.4. Multiperspective Decision Making and Multiperspective Learning
• 2.5. Dynamic and Interactive Decision Making
• 2.6. The Systemic Learning Framework
• 2.7. System Analysis
• 2.8. Case Study: Need of Systemic Learning in the Hospitality Industry
• 2.9. Summary.

• ch. 3. : Reinforcement Learning
• 3.1. Introduction
• 3.2. Learning Agents
• 3.3. Returns and Reward Calculations
• 3.4. Reinforcement Learning and Adaptive Control
• 3.5. Dynamic Systems
• 3.6. Reinforcement Learning and Control
• 3.7. Markov Property and Markov Decision Process
• 3.8. Value Functions
• 3.9. Learning An Optimal Policy (Model-Based and Model-Free Methods)
• 3.10. Dynamic Programming
• 3.11. Adaptive Dynamic Programming
• 3.12. Example: Reinforcement Learning for Boxing Trainer
• 3.13. Summary
• Reference.

• ch. 4: Systemic Machine Learning and Model
• 4.1. Introduction
• 4.2. A Framework for Systemic Learning
• 4.3. Capturing THE Systemic View
• 4.4. Mathematical Representation of System Interactions
• 4.5. Impact Function
• 4.6. Decision-Impact Analysis
• 4.7. Summary.

• ch. 5: Inference and Information Integration
• 5.1. Introduction
• 5.2. Inference Mechanisms and Need
• 5.3. Integration of Context and Inference
• 5.4. Statistical Inference and Induction
• 5.5. Pure Likelihood Approach
• 5.6. Bayesian Paradigm and Inference
• 5.7. Time-Based Inference
• 5.8. Inference to Build a System View
• 5.9. Summary.

• ch. 6: Adaptive Learning
• 6.1. Introduction
• 6.2. Adaptive Learning and Adaptive Systems
• 6.3. What is Adaptive Machine Learning?
• 6.4. Adaptation and Learning Method Selection Based on Scenario
• 6.5. Systemic Learning and Adaptive Learning
• 6.6. Competitive Learning and Adaptive Learning
• 6.7. Examples
• 6.8. Summary.

• ch. 7: Multiperspective and Whole-System Learning
• 7.1. Introduction
• 7.2. Multiperspective Context Building
• 7.3. Multiperspective Decision Making and Multiperspective Learning
• 7.4. Whole-System Learning and Multiperspective Approaches
• 7.5. Case Study Based on Multiperspective Approach
• 7.6. Limitations to a Multiperspective Approach
• 7.7. Summary.

• ch. 8: Incremental Learning and Knowledge Representation
• 8.1. Introduction
• 8.2. Why Incremental Learning?
• 8.3. Learning from What Is Already Learned
• 8.4. Supervised Incremental Learning
• 8.5. Incremental Unsupervised Learning and Incremental Clustering
• 8.6. Semisupervised Incremental Learning
• 8.7. Incremental and Systemic Learning
• 8.8. Incremental Closeness Value and Learning Method
• 8.9. Learning and Decision-Making Model
• 8.10. Incremental Classification Techniques
• 8.11. Case Study: Incremental Document Classification
• 8.12. Summary.

• ch. 9 Knowledge Augmentation: A Machine Learning Perspective
• 9.1. Introduction
• 9.2. Brief History and Related Work
• 9.3. Knowledge Augmentation and Knowledge Elicitation
• 9.4. Life Cycle of Knowledge
• 9.5. Incremental Knowledge Representation
• 9.6. Case-Based Learning and Learning with Reference Knowledge Loss
• 9.7. Knowledge Augmentation: Techniques and Methods
• 9.8. Heuristic Learning
• 9.9. Systemic Machine Learning and Knowledge Augmentation
• 9.10. Knowledge Augmentation in Complex Learning Scenarios
• 9.11. Case Studies
• 9.12. Summary.

• ch. 10: Building a Learning System
• 10.1. Introduction
• 10.2. Systemic Learning System
• 10.3. Algorithm Selection
• 10.4. Knowledge Representation
• 10.4.1. Practical Scenarios and Case Study
• 10.5. Designing a Learning System
• 10.6. Making System to Behave Intelligently
• 10.7. Example-Based Learning
• 10.8. Holistic Knowledge Framework and Use of Reinforcement Learning
• 10.9. Intelligent Agents Deployment and Knowledge Acquisition and Reuse
• 10.10. Case-Based Learning: Human Emotion-Detection System
• 10.11. Holistic View in Complex Decision Problem
• 10.12. Knowledge Representation and Data Discovery
• 10.13. Components
• 10.14. Future of Learning Systems and Intelligent Systems
• 10.15. Summary
• Appendix A: Statistical Learning Methods
• Appendix B: Markov Processes.

Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals.
(source: Nielsen Book Data)

• Preface xv Acknowledgments xix About the Author xxi 1 Introduction to Reinforcement and Systemic Machine Learning 1 1
• .1. Introduction 1 1
• .2. Supervised, Unsupervised, and Semisupervised Machine Learning 2 1
• .3. Traditional Learning Methods and History of Machine Learning 4 1
• .4. What Is Machine Learning? 7 1
• .5. Machine-Learning Problem 8 1
• .6. Learning Paradigms 9 1
• .7. Machine-Learning Techniques and Paradigms 12 1
• .8. What Is Reinforcement Learning? 14 1
• .9. Reinforcement Function and Environment Function 16 1
• .10. Need of Reinforcement Learning 17 1
• .11. Reinforcement Learning and Machine Intelligence 17 1
• .12. What Is Systemic Learning? 18 1
• .13. What Is Systemic Machine Learning? 18 1
• .14. Challenges in Systemic Machine Learning 19 1
• .15. Reinforcement Machine Learning and Systemic Machine Learning 19 1
• .16. Case Study Problem Detection in a Vehicle 20 1
• .17. Summary 20 2 Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning 23 2
• .1. Introduction 23 2
• .2. What Is Systemic Machine Learning? 27 2
• .3. Generalized Systemic Machine-Learning Framework 30 2
• .4. Multiperspective Decision Making and Multiperspective Learning 33 2
• .5. Dynamic and Interactive Decision Making 43 2
• .6. The Systemic Learning Framework 47 2
• .7. System Analysis 52 2
• .8. Case Study: Need of Systemic Learning in the Hospitality Industry 54 2
• .9. Summary 55 3 Reinforcement Learning 57 3
• .1. Introduction 57 3
• .2. Learning Agents 60 3
• .3. Returns and Reward Calculations 62 3
• .4. Reinforcement Learning and Adaptive Control 63 3
• .5. Dynamic Systems 66 3
• .6. Reinforcement Learning and Control 68 3
• .7. Markov Property and Markov Decision Process 68 3
• .8. Value Functions 69 3.8
• .1. Action and Value 70 3
• .9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 70 3
• .10. Dynamic Programming 71 3
• .11. Adaptive Dynamic Programming 71 3
• .12. Example: Reinforcement Learning for Boxing Trainer 75 3
• .13. Summary 75 4 Systemic Machine Learning and Model 77 4
• .1. Introduction 77 4
• .2. A Framework for Systemic Learning 78 4
• .3. Capturing the Systemic View 86 4
• .4. Mathematical Representation of System Interactions 89 4
• .5. Impact Function 91 4
• .6. Decision-Impact Analysis 91 4
• .7. Summary 97 5 Inference and Information Integration 99 5
• .1. Introduction 99 5
• .2. Inference Mechanisms and Need 101 5
• .3. Integration of Context and Inference 107 5
• .4. Statistical Inference and Induction 111 5
• .5. Pure Likelihood Approach 112 5
• .6. Bayesian Paradigm and Inference 113 5
• .7. Time-Based Inference 114 5
• .8. Inference to Build a System View 114 5
• .9. Summary 118 6 Adaptive Learning 119 6
• .1. Introduction 119 6
• .2. Adaptive Learning and Adaptive Systems 119 6
• .3. What Is Adaptive Machine Learning? 123 6
• .4. Adaptation and Learning Method Selection Based on Scenario 124 6
• .5. Systemic Learning and Adaptive Learning 127 6
• .6. Competitive Learning and Adaptive Learning 140 6
• .7. Examples 146 6
• .8. Summary 149 7 Multiperspective and Whole-System Learning 151 7
• .1. Introduction 151 7
• .2. Multiperspective Context Building 152 7
• .3. Multiperspective Decision Making and Multiperspective Learning 154 7
• .4. Whole-System Learning and Multiperspective Approaches 164 7
• .5. Case Study Based on Multiperspective Approach 167 7
• .6. Limitations to a Multiperspective Approach 174 7
• .7. Summary 174 8 Incremental Learning and Knowledge Representation 177 8
• .1. Introduction 177 8
• .2. Why Incremental Learning? 178 8
• .3. Learning from What Is Already Learned... 180 8
• .4. Supervised Incremental Learning 191 8
• .5. Incremental Unsupervised Learning and Incremental Clustering 191 8
• .6. Semisupervised Incremental Learning 196 8
• .7. Incremental and Systemic Learning 199 8
• .8. Incremental Closeness Value and Learning Method 200 8
• .9. Learning and Decision-Making Model 205 8
• .10. Incremental Classification Techniques 206 8
• .11. Case Study: Incremental Document Classification 207 8
• .12. Summary 208 9 Knowledge Augmentation: A Machine Learning Perspective 209 9
• .1. Introduction 209 9
• .2. Brief History and Related Work 211 9
• .3. Knowledge Augmentation and Knowledge Elicitation 215 9
• .4. Life Cycle of Knowledge 217 9
• .5. Incremental Knowledge Representation 222 9
• .6. Case-Based Learning and Learning with Reference to Knowledge Loss 224 9
• .7. Knowledge Augmentation: Techniques and Methods 224 9
• .8. Heuristic Learning 228 9
• .9. Systemic Machine Learning and Knowledge Augmentation 229 9
• .10. Knowledge Augmentation in Complex Learning Scenarios 232 9
• .11. Case Studies 232 9
• .12. Summary 235 10 Building a Learning System 237 10
• .1. Introduction 237 10
• .2. Systemic Learning System 237 10
• .3. Algorithm Selection 242 10
• .4. Knowledge Representation 244 10
• .5. Designing a Learning System 245 10
• .6. Making System to Behave Intelligently 246 10
• .7. Example-Based Learning 246 10
• .8. Holistic Knowledge Framework and Use of Reinforcement Learning 246 10
• .9. Intelligent Agents--Deployment and Knowledge Acquisition and Reuse 250 10
• .10. Case-Based Learning: Human Emotion-Detection System 251 10
• .11. Holistic View in Complex Decision Problem 253 10
• .12. Knowledge Representation and Data Discovery 255 10
• .13. Components 258 10
• .14. Future of Learning Systems and Intelligent Systems 259 10
• .15. Summary 259 Appendix A: Statistical Learning Methods 261 Appendix B: Markov Processes 271 Index 281.
• (source: Nielsen Book Data)

Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals.
(source: Nielsen Book Data)