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

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

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
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
Dewey number
  • 006.3/1
  • 006.31
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
1
http://library.link/vocab/subjectName
  • Decision Making
  • TECHNOLOGY & ENGINEERING / Electronics / General
  • Science
  • Computer science
  • Reinforcement learning
  • Machine learning
  • Decision making
Label
Reinforcement and systemic machine learning for decision making, Parag Kulkarni, (electronic book)
Instantiates
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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?
  • ch. 1:
  • 1.9.
  • Reinforcement Function and Environment Function
  • 1.10.
  • Need of Reinforcement Learning
  • 1.11.
  • Reinforcement Learning and Machine Intelligence
  • 1.12.
  • What is Systemic Learning?
  • 1.13.
  • What Is Systemic Machine Learning?
  • Introduction to Reinforcement and 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 --
  • 1.1.
  • Introduction
  • 1.2.
  • Supervised, Unsupervised, and Semisupervised Machine Learning
  • 1.3.
  • Traditional Learning Methods and History of Machine Learning
  • 2.4.
  • Multiperspective Decision Making and Multiperspective Learning
  • 2.5.
  • Dynamic and Interactive Decision Making
  • 2.6.
  • The Systemic Learning Framework
  • 2.7.
  • System Analysis
  • 2.8.
  • Case Study:
  • ch. 2:
  • Need of Systemic Learning in the Hospitality Industry
  • 2.9.
  • Summary --
  • 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
  • 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.
  • ch. 3.
  • Value Functions
  • 3.9.
  • Learning An Optimal Policy (Model-Based and Model-Free Methods)
  • 3.10.
  • Dynamic Programming
  • 3.11.
  • Adaptive Dynamic Programming
  • 3.12.
  • Example:
  • Reinforcement Learning for Boxing Trainer
  • :
  • 3.13.
  • Summary
  • Reference --
  • Reinforcement Learning
  • 3.1.
  • Introduction
  • 3.2.
  • Learning Agents
  • 3.3.
  • 4.4.
  • Mathematical Representation of System Interactions
  • 4.5.
  • Impact Function
  • 4.6.
  • Decision-Impact Analysis
  • 4.7.
  • Summary --
  • ch. 4:
  • Systemic Machine Learning and Model
  • 4.1.
  • Introduction
  • 4.2.
  • A Framework for Systemic Learning
  • 4.3.
  • Capturing THE Systemic View
  • 5.4.
  • Statistical Inference and Induction
  • 5.5.
  • Pure Likelihood Approach
  • 5.6.
  • Bayesian Paradigm and Inference
  • 5.7.
  • Time-Based Inference
  • 5.8.
  • Inference to Build a System View
  • ch. 5:
  • 5.9.
  • Summary --
  • Inference and Information Integration
  • 5.1.
  • Introduction
  • 5.2.
  • Inference Mechanisms and Need
  • 5.3.
  • Integration of Context and Inference
  • 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 --
  • ch. 6:
  • Adaptive Learning
  • 6.1.
  • Introduction
  • 6.2.
  • Adaptive Learning and Adaptive Systems
  • 6.3.
  • What is Adaptive Machine Learning?
  • 7.4.
  • Whole-System Learning and Multiperspective Approaches
  • 7.5.
  • Case Study Based on Multiperspective Approach
  • 7.6.
  • Limitations to a Multiperspective Approach
  • 7.7.
  • Summary --
  • ch. 7:
  • Multiperspective and Whole-System Learning
  • 7.1.
  • Introduction
  • 7.2.
  • Multiperspective Context Building
  • 7.3.
  • Multiperspective Decision Making and Multiperspective Learning
  • 8.4.
  • Supervised Incremental Learning
  • 8.5.
  • Incremental Unsupervised Learning and Incremental Clustering
  • 8.6.
  • Semisupervised Incremental Learning
  • 8.7.
  • Incremental and Systemic Learning
  • 8.8.
  • Incremental Closeness Value and Learning Method
  • ch. 8:
  • 8.9.
  • Learning and Decision-Making Model
  • 8.10.
  • Incremental Classification Techniques
  • 8.11.
  • Case Study: Incremental Document Classification
  • 8.12.
  • Summary --
  • Incremental Learning and Knowledge Representation
  • 8.1.
  • Introduction
  • 8.2.
  • Why Incremental Learning?
  • 8.3.
  • Learning from What Is Already Learned
  • Life Cycle of Knowledge
  • 9.5.
  • Incremental Knowledge Representation
  • 9.6.
  • Case-Based Learning and Learning with Reference Knowledge Loss
  • 9.7.
  • Knowledge Augmentation: Techniques and Methods
  • 9.8.
  • Heuristic Learning
  • 9.9.
  • ch. 9 Knowledge Augmentation: A Machine Learning Perspective
  • Systemic Machine Learning and Knowledge Augmentation
  • 9.10.
  • Knowledge Augmentation in Complex Learning Scenarios
  • 9.11.
  • Case Studies
  • 9.12.
  • Summary --
  • 9.1.
  • Introduction
  • 9.2.
  • Brief History and Related Work
  • 9.3.
  • Knowledge Augmentation and Knowledge Elicitation
  • 9.4.
  • 10.4.
  • Knowledge Representation
  • 10.4.1.
  • Practical Scenarios and Case Study
  • 10.5.
  • Designing a Learning System
  • 10.6.
  • Making System to Behave Intelligently
  • 10.7.
  • Example-Based Learning
  • ch. 10:
  • 10.8.
  • Holistic Knowledge Framework and Use of Reinforcement Learning
  • 10.9.
  • Intelligent Agents Deployment and Knowledge Acquisition and Reuse
  • 10.10.
  • Case-Based Learning: Human Emotion-Detection System
  • 10.11.
  • Holistic View in Complex Decision Problem
  • 10.12.
  • Knowledge Representation and Data Discovery
  • Building a Learning System
  • 10.13.
  • Components
  • 10.14.
  • Future of Learning Systems and Intelligent Systems
  • 10.15.
  • Summary
  • Appendix A:
  • Statistical Learning Methods
  • Appendix B:
  • Markov Processes
  • 10.1.
  • Introduction
  • 10.2.
  • Systemic Learning System
  • 10.3.
  • Algorithm Selection
Extent
1 online resource (422 p.)
Form of item
online
Isbn
9781118271537
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
9786613807076
Specific material designation
remote
System control number
ocn801366216
Label
Reinforcement and systemic machine learning for decision making, Parag Kulkarni, (electronic book)
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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?
  • ch. 1:
  • 1.9.
  • Reinforcement Function and Environment Function
  • 1.10.
  • Need of Reinforcement Learning
  • 1.11.
  • Reinforcement Learning and Machine Intelligence
  • 1.12.
  • What is Systemic Learning?
  • 1.13.
  • What Is Systemic Machine Learning?
  • Introduction to Reinforcement and 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 --
  • 1.1.
  • Introduction
  • 1.2.
  • Supervised, Unsupervised, and Semisupervised Machine Learning
  • 1.3.
  • Traditional Learning Methods and History of Machine Learning
  • 2.4.
  • Multiperspective Decision Making and Multiperspective Learning
  • 2.5.
  • Dynamic and Interactive Decision Making
  • 2.6.
  • The Systemic Learning Framework
  • 2.7.
  • System Analysis
  • 2.8.
  • Case Study:
  • ch. 2:
  • Need of Systemic Learning in the Hospitality Industry
  • 2.9.
  • Summary --
  • 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
  • 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.
  • ch. 3.
  • Value Functions
  • 3.9.
  • Learning An Optimal Policy (Model-Based and Model-Free Methods)
  • 3.10.
  • Dynamic Programming
  • 3.11.
  • Adaptive Dynamic Programming
  • 3.12.
  • Example:
  • Reinforcement Learning for Boxing Trainer
  • :
  • 3.13.
  • Summary
  • Reference --
  • Reinforcement Learning
  • 3.1.
  • Introduction
  • 3.2.
  • Learning Agents
  • 3.3.
  • 4.4.
  • Mathematical Representation of System Interactions
  • 4.5.
  • Impact Function
  • 4.6.
  • Decision-Impact Analysis
  • 4.7.
  • Summary --
  • ch. 4:
  • Systemic Machine Learning and Model
  • 4.1.
  • Introduction
  • 4.2.
  • A Framework for Systemic Learning
  • 4.3.
  • Capturing THE Systemic View
  • 5.4.
  • Statistical Inference and Induction
  • 5.5.
  • Pure Likelihood Approach
  • 5.6.
  • Bayesian Paradigm and Inference
  • 5.7.
  • Time-Based Inference
  • 5.8.
  • Inference to Build a System View
  • ch. 5:
  • 5.9.
  • Summary --
  • Inference and Information Integration
  • 5.1.
  • Introduction
  • 5.2.
  • Inference Mechanisms and Need
  • 5.3.
  • Integration of Context and Inference
  • 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 --
  • ch. 6:
  • Adaptive Learning
  • 6.1.
  • Introduction
  • 6.2.
  • Adaptive Learning and Adaptive Systems
  • 6.3.
  • What is Adaptive Machine Learning?
  • 7.4.
  • Whole-System Learning and Multiperspective Approaches
  • 7.5.
  • Case Study Based on Multiperspective Approach
  • 7.6.
  • Limitations to a Multiperspective Approach
  • 7.7.
  • Summary --
  • ch. 7:
  • Multiperspective and Whole-System Learning
  • 7.1.
  • Introduction
  • 7.2.
  • Multiperspective Context Building
  • 7.3.
  • Multiperspective Decision Making and Multiperspective Learning
  • 8.4.
  • Supervised Incremental Learning
  • 8.5.
  • Incremental Unsupervised Learning and Incremental Clustering
  • 8.6.
  • Semisupervised Incremental Learning
  • 8.7.
  • Incremental and Systemic Learning
  • 8.8.
  • Incremental Closeness Value and Learning Method
  • ch. 8:
  • 8.9.
  • Learning and Decision-Making Model
  • 8.10.
  • Incremental Classification Techniques
  • 8.11.
  • Case Study: Incremental Document Classification
  • 8.12.
  • Summary --
  • Incremental Learning and Knowledge Representation
  • 8.1.
  • Introduction
  • 8.2.
  • Why Incremental Learning?
  • 8.3.
  • Learning from What Is Already Learned
  • Life Cycle of Knowledge
  • 9.5.
  • Incremental Knowledge Representation
  • 9.6.
  • Case-Based Learning and Learning with Reference Knowledge Loss
  • 9.7.
  • Knowledge Augmentation: Techniques and Methods
  • 9.8.
  • Heuristic Learning
  • 9.9.
  • ch. 9 Knowledge Augmentation: A Machine Learning Perspective
  • Systemic Machine Learning and Knowledge Augmentation
  • 9.10.
  • Knowledge Augmentation in Complex Learning Scenarios
  • 9.11.
  • Case Studies
  • 9.12.
  • Summary --
  • 9.1.
  • Introduction
  • 9.2.
  • Brief History and Related Work
  • 9.3.
  • Knowledge Augmentation and Knowledge Elicitation
  • 9.4.
  • 10.4.
  • Knowledge Representation
  • 10.4.1.
  • Practical Scenarios and Case Study
  • 10.5.
  • Designing a Learning System
  • 10.6.
  • Making System to Behave Intelligently
  • 10.7.
  • Example-Based Learning
  • ch. 10:
  • 10.8.
  • Holistic Knowledge Framework and Use of Reinforcement Learning
  • 10.9.
  • Intelligent Agents Deployment and Knowledge Acquisition and Reuse
  • 10.10.
  • Case-Based Learning: Human Emotion-Detection System
  • 10.11.
  • Holistic View in Complex Decision Problem
  • 10.12.
  • Knowledge Representation and Data Discovery
  • Building a Learning System
  • 10.13.
  • Components
  • 10.14.
  • Future of Learning Systems and Intelligent Systems
  • 10.15.
  • Summary
  • Appendix A:
  • Statistical Learning Methods
  • Appendix B:
  • Markov Processes
  • 10.1.
  • Introduction
  • 10.2.
  • Systemic Learning System
  • 10.3.
  • Algorithm Selection
Extent
1 online resource (422 p.)
Form of item
online
Isbn
9781118271537
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
9786613807076
Specific material designation
remote
System control number
ocn801366216

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