Reinforcement and Systemic Machine Learning for Decision Making - Hardcover

Kulkarni, Parag

Hardcover

ISBN 10: 047091999X ISBN 13: 9780470919996

Publisher: Wiley-IEEE Press, 2012

Synopsis

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.

About this title

About the Author

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.

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.

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.

Edition details

• Publisher: Wiley-IEEE Press
• Publication date: 2012
• Language: English
• ISBN 10: 047091999X
• ISBN 13: 9780470919996
• Binding: Hardcover
• Edition number: 1
• Number of pages: 320