The Resource Reinforcement and systemic machine learning for decision making, Parag Kulkarni, (electronic resource)

Reinforcement and systemic machine learning for decision making, Parag Kulkarni, (electronic resource)

Label
Reinforcement and systemic machine learning for decision making
Title
Reinforcement and systemic machine learning for decision making
Statement of responsibility
Parag Kulkarni
Creator
Subject
Genre
Language
eng
Summary
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
Member of
Cataloging source
EBLCP
http://library.link/vocab/creatorName
Kulkarni, Parag
Index
no index present
LC call number
Q325.6
LC item number
.K85 2012
Literary form
non fiction
Nature of contents
dictionaries
Series statement
IEEE Press Series on Systems Science and Engineering
Series volume
v.1
http://library.link/vocab/subjectName
  • Decision Making
  • Machine learning
  • Reinforcement learning
  • TECHNOLOGY & ENGINEERING / Electronics / General
  • Science
  • Computer science
  • Reinforcement learning
  • Machine learning
  • Decision making
  • COMPUTERS / Enterprise Applications / Business Intelligence Tools
  • COMPUTERS / Intelligence (AI) & Semantics
Label
Reinforcement and systemic machine learning for decision making, Parag Kulkarni, (electronic resource)
Link
http://libraries.ou.edu/access.aspx?url=http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266787
Instantiates
Publication
Note
7.3 Multiperspective Decision Making And Multiperspective Learning
Color
not applicable
Contents
  • 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
  • 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
  • 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
  • 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
  • 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
Extent
1 online resource (422 p.)
Form of item
online
Isbn
9781118271537
Isbn Type
(electronic bk.)
Other control number
9786613807076
Specific material designation
remote
System control number
  • 4078822-01okla_normanlaw
  • (SIRSI)4078822
  • (Sirsi) o801366216
  • (OCoLC)801366216
Label
Reinforcement and systemic machine learning for decision making, Parag Kulkarni, (electronic resource)
Link
http://libraries.ou.edu/access.aspx?url=http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266787
Publication
Note
7.3 Multiperspective Decision Making And Multiperspective Learning
Color
not applicable
Contents
  • 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
  • 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
  • 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
  • 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
  • 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
Extent
1 online resource (422 p.)
Form of item
online
Isbn
9781118271537
Isbn Type
(electronic bk.)
Other control number
9786613807076
Specific material designation
remote
System control number
  • 4078822-01okla_normanlaw
  • (SIRSI)4078822
  • (Sirsi) o801366216
  • (OCoLC)801366216

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