**Reinforcement and Systemic Machine Learning for Decision Making**There 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. Content: Chapter 1 Introduction to Reinforcement and Systemic Machine Learning (pages 1–21): Chapter 2 Fundamentals of Whole?System, Systemic, and Multiperspective Machine Learning (pages 23–56): Chapter 3 Reinforcement Learning (pages 57–76): Chapter 4 Systemic Machine Learning and Model (pages 77–98): Chapter 5 Inference and Information Integration (pages 99–118): Chapter 6 Adaptive Learning (pages 119–149): Chapter 7 Multiperspective and Whole?System Learning (pages 151–175): Chapter 8 Incremental Learning and Knowledge Representation (pages 177–208): Chapter 9 Knowledge Augmentation: A Machine Learning Perspective (pages 209–236): Chapter 10 Building a Learning System (pages 237–260): Reinforcement and Systemic Machine Learning for Decision Making There 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. Content: Chapter 1 Introduction to Reinforcement and Systemic Machine Learning (pages 1–21): Chapter 2 Fundamentals of Whole?System, Systemic, and Multiperspective Machine Learning (pages 23–56): Chapter 3 Reinforcement Learning (pages 57–76): Chapter 4 Systemic Machine Learning and Model (pages 77–98): Chapter 5 Inference and Information Integration (pages 99–118): Chapter 6 Adaptive Learning (pages 119–149): Chapter 7 Multiperspective and Whole?System Learning (pages 151–175): Chapter 8 Incremental Learning and Knowledge Representation (pages 177–208): Chapter 9 Knowledge Augmentation: A Machine Learning Perspective (pages 209–236): Chapter 10 Building a Learning System (pages 237–260):
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.
"Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals"-- "The book focuses on machine learning and systemic machine learning -- a specialized research area in the field of machine learning"-- "Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals"-- Provided by publisher * Authors have both industrial and academic experiences * Case studies are included reflecting author's industrial experiences * Downloadable tutorials are available . "The book focuses on machine learning and systemic machine learning -- a specialized research area in the field of machine learning"-- Provided by publisher
