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Informationen zum Autor Parag Kulkarni , PhD, DSc, is the founder and Chief Scientist of EKLat Research where he has empowered businesses through machine learning, knowledge management, and systemic management. He has been working within the IT industry for over twenty years. The recipient of several awards, Dr. Kulkarni is a pioneer in the field. His areas of research and product development include M-maps, intelligent systems, text mining, image processing, decision systems, forecasting, IT strategy, artificial intelligence, and machine learning. Dr. Kulkarni has over 100 research publications including several books. Klappentext 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 growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making.Chapters include:* Introduction to Reinforcement and Systemic Machine Learning* Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning* Systemic Machine Learning and Model* Inference and Information Integration* Adaptive Learning* Incremental Learning and Knowledge Representation* Knowledge Augmentation: A Machine Learning Perspective* Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource. Zusammenfassung * Authors have both industrial and academic experiences * Case studies are included reflecting author's industrial experiences * Downloadable tutorials are available . Inhaltsverzeichnis 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
