Reinforcement and systemic machine learning for decision making / (Record no. 40510)

MARC details
000 -LEADER
fixed length control field 08823nam a2201045 i 4500
001 - CONTROL NUMBER
control field 6266787
003 - CONTROL NUMBER IDENTIFIER
control field IEEE
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230927112354.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr |n|||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151221s2012 nju ob 001 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781118266502
Qualifying information ebook
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 047091999X
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9780470919996
Qualifying information print
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 1118266501
Qualifying information electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781118271537
Qualifying information electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 111827153X
Qualifying information electronic
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1002/9781118266502
Source of number or code doi
035 ## - SYSTEM CONTROL NUMBER
System control number (CaBNVSL)mat06266787
035 ## - SYSTEM CONTROL NUMBER
System control number (IDAMS)0b000064818b36d1
040 ## - CATALOGING SOURCE
Original cataloging agency CaBNVSL
Language of cataloging eng
Description conventions rda
Transcribing agency CaBNVSL
Modifying agency CaBNVSL
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/1
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Kulkarni, Parag,
Relator term author.
245 10 - TITLE STATEMENT
Title Reinforcement and systemic machine learning for decision making /
Statement of responsibility, etc. Parag Kulkarni.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Hoboken [New Jersey] :
Name of producer, publisher, distributor, manufacturer John Wiley & Sons,
Date of production, publication, distribution, manufacture, or copyright notice c2012.
264 #2 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture [Piscataqay, New Jersey] :
Name of producer, publisher, distributor, manufacturer IEEE Xplore,
Date of production, publication, distribution, manufacture, or copyright notice [2012]
300 ## - PHYSICAL DESCRIPTION
Extent 1 PDF (422 pages).
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
337 ## - MEDIA TYPE
Media type term electronic
Source isbdmedia
338 ## - CARRIER TYPE
Carrier type term online resource
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement IEEE Press Series on Systems Science and Engineering ;
Volume/sequential designation v.1
500 ## - GENERAL NOTE
General note In Wiley online library
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Preface xv -- Acknowledgments xix -- About the Author xxi -- 1 Introduction to Reinforcement and Systemic Machine Learning 1 -- 1.1. Introduction 1 -- 1.2. Supervised, Unsupervised, and Semisupervised Machine Learning 2 -- 1.3. Traditional Learning Methods and History of Machine Learning 4 -- 1.4. What Is Machine Learning? 7 -- 1.5. Machine-Learning Problem 8 -- 1.6. Learning Paradigms 9 -- 1.7. Machine-Learning Techniques and Paradigms 12 -- 1.8. What Is Reinforcement Learning? 14 -- 1.9. Reinforcement Function and Environment Function 16 -- 1.10. Need of Reinforcement Learning 17 -- 1.11. Reinforcement Learning and Machine Intelligence 17 -- 1.12. What Is Systemic Learning? 18 -- 1.13. What Is Systemic Machine Learning? 18 -- 1.14. Challenges in Systemic Machine Learning 19 -- 1.15. Reinforcement Machine Learning and Systemic Machine Learning 19 -- 1.16. Case Study Problem Detection in a Vehicle 20 -- 1.17. Summary 20 -- 2 Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning 23 -- 2.1. Introduction 23 -- 2.2. What Is Systemic Machine Learning? 27 -- 2.3. Generalized Systemic Machine-Learning Framework 30 -- 2.4. Multiperspective Decision Making and Multiperspective Learning 33 -- 2.5. Dynamic and Interactive Decision Making 43 -- 2.6. The Systemic Learning Framework 47 -- 2.7. System Analysis 52 -- 2.8. Case Study: Need of Systemic Learning in the Hospitality Industry 54 -- 2.9. Summary 55 -- 3 Reinforcement Learning 57 -- 3.1. Introduction 57 -- 3.2. Learning Agents 60 -- 3.3. Returns and Reward Calculations 62 -- 3.4. Reinforcement Learning and Adaptive Control 63 -- 3.5. Dynamic Systems 66 -- 3.6. Reinforcement Learning and Control 68 -- 3.7. Markov Property and Markov Decision Process 68 -- 3.8. Value Functions 69 -- 3.8.1. Action and Value 70 -- 3.9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 70 -- 3.10. Dynamic Programming 71 -- 3.11. Adaptive Dynamic Programming 71 -- 3.12. Example: Reinforcement Learning for Boxing Trainer 75.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 3.13. Summary 75 -- 4 Systemic Machine Learning and Model 77 -- 4.1. Introduction 77 -- 4.2. A Framework for Systemic Learning 78 -- 4.3. Capturing the Systemic View 86 -- 4.4. Mathematical Representation of System Interactions 89 -- 4.5. Impact Function 91 -- 4.6. Decision-Impact Analysis 91 -- 4.7. Summary 97 -- 5 Inference and Information Integration 99 -- 5.1. Introduction 99 -- 5.2. Inference Mechanisms and Need 101 -- 5.3. Integration of Context and Inference 107 -- 5.4. Statistical Inference and Induction 111 -- 5.5. Pure Likelihood Approach 112 -- 5.6. Bayesian Paradigm and Inference 113 -- 5.7. Time-Based Inference 114 -- 5.8. Inference to Build a System View 114 -- 5.9. Summary 118 -- 6 Adaptive Learning 119 -- 6.1. Introduction 119 -- 6.2. Adaptive Learning and Adaptive Systems 119 -- 6.3. What Is Adaptive Machine Learning? 123 -- 6.4. Adaptation and Learning Method Selection Based on Scenario 124 -- 6.5. Systemic Learning and Adaptive Learning 127 -- 6.6. Competitive Learning and Adaptive Learning 140 -- 6.7. Examples 146 -- 6.8. Summary 149 -- 7 Multiperspective and Whole-System Learning 151 -- 7.1. Introduction 151 -- 7.2. Multiperspective Context Building 152 -- 7.3. Multiperspective Decision Making and Multiperspective Learning 154 -- 7.4. Whole-System Learning and Multiperspective Approaches 164 -- 7.5. Case Study Based on Multiperspective Approach 167 -- 7.6. Limitations to a Multiperspective Approach 174 -- 7.7. Summary 174 -- 8 Incremental Learning and Knowledge Representation 177 -- 8.1. Introduction 177 -- 8.2. Why Incremental Learning? 178 -- 8.3. Learning from What Is Already Learned. . . 180 -- 8.4. Supervised Incremental Learning 191 -- 8.5. Incremental Unsupervised Learning and Incremental Clustering 191 -- 8.6. Semisupervised Incremental Learning 196 -- 8.7. Incremental and Systemic Learning 199 -- 8.8. Incremental Closeness Value and Learning Method 200 -- 8.9. Learning and Decision-Making Model 205 -- 8.10. Incremental Classification Techniques 206.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 8.11. Case Study: Incremental Document Classification 207 -- 8.12. Summary 208 -- 9 Knowledge Augmentation: A Machine Learning Perspective 209 -- 9.1. Introduction 209 -- 9.2. Brief History and Related Work 211 -- 9.3. Knowledge Augmentation and Knowledge Elicitation 215 -- 9.4. Life Cycle of Knowledge 217 -- 9.5. Incremental Knowledge Representation 222 -- 9.6. Case-Based Learning and Learning with Reference to Knowledge Loss 224 -- 9.7. Knowledge Augmentation: Techniques and Methods 224 -- 9.8. Heuristic Learning 228 -- 9.9. Systemic Machine Learning and Knowledge Augmentation 229 -- 9.10. Knowledge Augmentation in Complex Learning Scenarios 232 -- 9.11. Case Studies 232 -- 9.12. Summary 235 -- 10 Building a Learning System 237 -- 10.1. Introduction 237 -- 10.2. Systemic Learning System 237 -- 10.3. Algorithm Selection 242 -- 10.4. Knowledge Representation 244 -- 10.5. Designing a Learning System 245 -- 10.6. Making System to Behave Intelligently 246 -- 10.7. Example-Based Learning 246 -- 10.8. Holistic Knowledge Framework and Use of Reinforcement Learning 246 -- 10.9. Intelligent Agents-Deployment and Knowledge Acquisition and Reuse 250 -- 10.10. Case-Based Learning: Human Emotion-Detection System 251 -- 10.11. Holistic View in Complex Decision Problem 253 -- 10.12. Knowledge Representation and Data Discovery 255 -- 10.13. Components 258 -- 10.14. Future of Learning Systems and Intelligent Systems 259 -- 10.15. Summary 259 -- Appendix A: Statistical Learning Methods 261 -- Appendix B: Markov Processes 271 -- Index 281.
506 1# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Restricted to subscribers or individual electronic text purchasers.
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Also available in print.
538 ## - SYSTEM DETAILS NOTE
System details note Mode of access: World Wide Web
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on PDF viewed 12/21/2015.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Reinforcement learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Decision Making.
655 #0 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
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-- Abstracts
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-- Actuators
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-- Adaptation models
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-- Adaptive systems
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-- Bayesian methods
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-- Buildings
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-- Context
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-- Context modeling
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-- Decision making
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-- Equations
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-- Heuristic algorithms
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-- Humans
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-- Indexes
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-- Inference mechanisms
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-- Integrated circuits
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-- Intelligent agents
695 ## -
-- Intelligent systems
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-- Knowledge acquisition
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-- Knowledge based systems
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-- Knowledge representation
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-- Learning
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-- Learning systems
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-- Machine learning
695 ## -
-- Machine learning algorithms
695 ## -
-- Magnetic heads
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-- Markov processes
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-- Mathematical model
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-- Neural networks
695 ## -
-- Probabilistic logic
695 ## -
-- Roads
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-- Sensors
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-- Standards
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-- Statistical learning
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-- Steady-state
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-- Supervised learning
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-- Switches
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-- Training
695 ## -
-- Training data
695 ## -
-- Unsupervised learning
695 ## -
-- Vectors
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element IEEE Xplore (Online Service),
Relator term distributor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element John Wiley & Sons,
Relator term publisher.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
International Standard Book Number 9780470919996
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title IEEE Press Series on Systems Science and Engineering ;
Volume/sequential designation v.1
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Abstract with links to resource
Uniform Resource Identifier <a href="https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266787">https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266787</a>

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