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Reinforcement and systemic machine learning for decision making

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Reinforcement and systemic machine learning for decision making/ Parag Kulkarni.
Author:	Kulkarni, Parag.
Published:	Hoboken :John Wiley & Sons, : c2012.,
Description:	1 online resource (422 p.)
Notes:	7.3 Multiperspective Decision Making And Multiperspective Learning
Subject:	Decision Making. -
Online resource:	http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266787
ISBN:	9781118266502 (electronic bk.)

Reinforcement and systemic machine learning for decision making
Kulkarni, Parag.

Reinforcement and systemic machine learning for decision making[electronic resource] /Parag Kulkarni. - Hoboken :John Wiley & Sons,c2012. - 1 online resource (422 p.) - IEEE Press Series on Systems Science and Engineering ;v.1. - IEEE Press series on systems science and engineering..

7.3 Multiperspective Decision Making And Multiperspective Learning

Chapter 1: Introduction to Reinforcement and Systemic Machine Learning; 1.1 Introduction; 1.2 Supervised, Unsupervised, and Semisupervised Machine Learning; 1.3 Traditional Learning Methods and History of Machine Learning; 1.4 What is Machine Learning?; 1.5 Machine-Learning Problem; 1.6 Learning Paradigms; 1.7 Machine-Learning Techniques and Paradigms; 1.8 What is Reinforcement Learning?; 1.9 Reinforcement Function and Environment Function; 1.10 Need of Reinforcement Learning

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 g.

ISBN: 9781118266502 (electronic bk.)

Standard No.: 9786613807076Subjects--Topical Terms:

789088
Decision Making.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: Q325.6 / .K85 2012

Dewey Class. No.: 006.3/1

Reinforcement and systemic machine learning for decision making
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245 1 0 $a Reinforcement and systemic machine learning for decision making $h [electronic resource] / $c Parag Kulkarni.
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300 $a 1 online resource (422 p.)
490 1 $a IEEE Press Series on Systems Science and Engineering ; $v v.1
500 $a 7.3 Multiperspective Decision Making And Multiperspective Learning
505 0 $a Chapter 1: Introduction to Reinforcement and Systemic Machine Learning; 1.1 Introduction; 1.2 Supervised, Unsupervised, and Semisupervised Machine Learning; 1.3 Traditional Learning Methods and History of Machine Learning; 1.4 What is Machine Learning?; 1.5 Machine-Learning Problem; 1.6 Learning Paradigms; 1.7 Machine-Learning Techniques and Paradigms; 1.8 What is Reinforcement Learning?; 1.9 Reinforcement Function and Environment Function; 1.10 Need of Reinforcement Learning
505 8 $a 1.11 Reinforcement Learning and Machine Intelligence1.12 What is Systemic Learning?; 1.13 What Is Systemic Machine Learning?; 1.14 Challenges in Systemic Machine Learning; 1.15 Reinforcement Machine Learning and Systemic Machine Learning; 1.16 Case Study Problem Detection in a Vehicle; 1.17 Summary; Reference; Chapter 2: Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning; 2.1 Introduction; 2.2 What is Systemic Machine Learning?; 2.3 Generalized Systemic Machine-Learning Framework; 2.4 Multiperspective Decision Making and Multiperspective Learning
505 8 $a 2.5 Dynamic and Interactive Decision Making2.6 The Systemic Learning Framework; 2.7 System Analysis; 2.8 Case Study: Need of Systemic Learning in the Hospitality Industry; 2.9 Summary; References; Chapter 3: Reinforcement Learning; 3.1 Introduction; 3.2 Learning Agents; 3.3 Returns and Reward Calculations; 3.4 Reinforcement Learning and Adaptive Control; 3.5 Dynamic Systems; 3.6 Reinforcement Learning and Control; 3.7 Markov Property and Markov Decision Process; 3.8 Value Functions; 3.9 Learning An Optimal Policy (Model-Based and Model-Free Methods); 3.10 Dynamic Programming
505 8 $a 3.11 Adaptive Dynamic Programming3.12 Example: Reinforcement Learning for Boxing Trainer; 3.13 Summary; Reference; Chapter 4: Systemic Machine Learning and Model; 4.1 Introduction; 4.2 A Framework for Systemic Learning; 4.3 Capturing THE Systemic View; 4.4 Mathematical Representation of System Interactions; 4.5 Impact Function; 4.6 Decision-Impact Analysis; 4.7 Summary; Chapter 5: Inference and Information Integration; 5.1 Introduction; 5.2 Inference Mechanisms and Need; 5.3 Integration of Context and Inference; 5.4 Statistical Inference and Induction; 5.5 Pure Likelihood Approach
505 8 $a 5.6 Bayesian Paradigm and Inference5.7 Time-Based Inference; 5.8 Inference to Build a System View; 5.9 Summary; References; Chapter 6: Adaptive Learning; 6.1 Introduction; 6.2 Adaptive Learning and Adaptive Systems; 6.3 What is Adaptive Machine Learning?; 6.4 Adaptation and Learning Method Selection Based on Scenario; 6.5 Systemic Learning and Adaptive Learning; 6.6 Competitive Learning and Adaptive Learning; 6.7 Examples; 6.8 Summary; References; Chapter 7: Multiperspective and Whole-System Learning; 7.1 Introduction; 7.2 Multiperspective Context Building
520 $a 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 g.
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