Reinforcement and Systemic Machine Learning for Decision Making

Name: Reinforcement and Systemic Machine Learning for Decision Making
Brand: John Wiley & Sons Inc
SKU: 9780470919996
Price: 3051 CZK
Availability: InStock

Autor: Parag Kulkarni, Bithika Samanta

Jazyk:

Vazba: Pevná

Vydavatel: John Wiley & Sons Inc

Dostupnost: Skladem u dodavatele

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Reinforcement and Systemic Machine Learning for Decision Making§There are always difficulties in mak...

Informace o knize

Autor

Parag Kulkarni, Bithika Samanta

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2012

Stránek

312

EAN

9780470919996

Enbook ID

01389079

Vydavatel

John Wiley & Sons Inc

Hmotnost

582

Rozměry

164 x 242 x 22

Kompletní popis

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.

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