Reinforcement and systemic machine learning for decision making

 Content: 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.
 Abstract:             
              Reinforcement and Systemic Machine Learning for Decision Making  explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence.                 Read more...