Reinforcement and systemic machine learning for decision making / (Record no. 59841)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 08842nam a2201057 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 6266787 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421114418.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151221s2012 nju ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781118266502 |
| -- | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 047091999X |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3/1 |
| 100 1# - AUTHOR NAME | |
| Author | Kulkarni, Parag, |
| 245 10 - TITLE STATEMENT | |
| Title | Reinforcement and systemic machine learning for decision making / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (422 pages). |
| 490 1# - SERIES STATEMENT | |
| Series statement | IEEE Press Series on Systems Science and Engineering ; |
| 500 ## - GENERAL NOTE | |
| Remark 1 | In Wiley online library |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266787 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Hoboken [New Jersey] : |
| -- | John Wiley & Sons, |
| -- | c2012. |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [2012] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/21/2015. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Reinforcement learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Decision Making. |
| 695 ## - | |
| -- | Abstracts |
| 695 ## - | |
| -- | Actuators |
| 695 ## - | |
| -- | Adaptation models |
| 695 ## - | |
| -- | Adaptive systems |
| 695 ## - | |
| -- | Bayesian methods |
| 695 ## - | |
| -- | Buildings |
| 695 ## - | |
| -- | Context |
| 695 ## - | |
| -- | Context modeling |
| 695 ## - | |
| -- | Decision making |
| 695 ## - | |
| -- | Equations |
| 695 ## - | |
| -- | Heuristic algorithms |
| 695 ## - | |
| -- | Humans |
| 695 ## - | |
| -- | Indexes |
| 695 ## - | |
| -- | Inference mechanisms |
| 695 ## - | |
| -- | Integrated circuits |
| 695 ## - | |
| -- | Intelligent agents |
| 695 ## - | |
| -- | Intelligent systems |
| 695 ## - | |
| -- | Knowledge acquisition |
| 695 ## - | |
| -- | Knowledge based systems |
| 695 ## - | |
| -- | Knowledge representation |
| 695 ## - | |
| -- | Learning |
| 695 ## - | |
| -- | Learning systems |
| 695 ## - | |
| -- | Machine learning |
| 695 ## - | |
| -- | Machine learning algorithms |
| 695 ## - | |
| -- | Magnetic heads |
| 695 ## - | |
| -- | Markov processes |
| 695 ## - | |
| -- | Mathematical model |
| 695 ## - | |
| -- | Neural networks |
| 695 ## - | |
| -- | Probabilistic logic |
| 695 ## - | |
| -- | Roads |
| 695 ## - | |
| -- | Sensors |
| 695 ## - | |
| -- | Standards |
| 695 ## - | |
| -- | Statistical learning |
| 695 ## - | |
| -- | Steady-state |
| 695 ## - | |
| -- | Supervised learning |
| 695 ## - | |
| -- | Switches |
| 695 ## - | |
| -- | Training |
| 695 ## - | |
| -- | Training data |
| 695 ## - | |
| -- | Unsupervised learning |
| 695 ## - | |
| -- | Vectors |
No items available.