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作者 Kulkarni, Parag
書名 Reinforcement and systemic machine learning for decision making [electronic resource] / Parag Kulkarni
出版項 Hoboken : John Wiley & Sons, 2012
國際標準書號 9781118266502 (electronic bk.)
1118266501 (electronic bk.)
9781118271537 (electronic bk.)
111827153X (electronic bk.)
9780470919996
047091999X
國際標準號碼 9786613807076
book jacket
說明 1 online resource (422 p.)
系列 IEEE Press Series on Systems Science and Engineering ; v.1
IEEE Press series on systems science and engineering
附註 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 MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available--or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm--creating new learning applications and, ultimately, more intelligent machines.The first book of its kind in this new and g
Description based upon print version of record
主題 Reinforcement learning
Machine learning
Decision making
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