Reinforcement and Systemic Machine Learning for Decision Making

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Reinforcement and Systemic Machine Learning for DecisionMaking There are always difficulties in making machines that learn fromexperience. Complete information is not always available--orit becomes available in bits and pieces over a period of time. Withrespect to systemic learning, there is a need to understand theimpact of decisions and actions on a system over that period oftime. This book takes a holistic approach to addressing that needand presents a new paradigm--creating new learningapplications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field,Reinforcement and Systemic Machine Learning for Decision Makingfocuses on the specialized research area of machine learning andsystemic machine learning. It addresses reinforcement learning andits applications, incremental machine learning, repetitivefailure-correction mechanisms, and multiperspective decisionmaking. Chapters include: * Introduction to Reinforcement and Systemic MachineLearning * Fundamentals of Whole-System, Systemic, and MultiperspectiveMachine 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 paradigmto become one of the more utilized in its field, professionals inthe area of machine and systemic learning will find this book to bea valuable resource.

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Parag Kulkarni

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Computer Science Computer Science Special Topics Electrical & Electronics Engineering Elektrotechnik u. Elektronik Informatik Intelligent Systems & Agents Intelligente Systeme u. Agenten Neural Networks Neuronale Netze Spezialthemen Informatik

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