Reinforcement and systemic machine learning for decision making /

• Κύριος συγγραφέας: Kulkarni, Parag
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken : John Wiley & Sons, [2012]
• Σειρά: IEEE Press series on systems science and engineering.
• Θέματα: Reinforcement learning.; Machine learning.; Decision making.; Decision Making.; TECHNOLOGY & ENGINEERING > Electronics > General.; Science.; Computer science.; COMPUTERS > Enterprise Applications > Business Intelligence Tools.; COMPUTERS > Intelligence (AI) & Semantics.; Electronic books.

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 ne...

Πίνακας περιεχομένων:

• 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.