Coverart for item
The Resource Machine learning techniques for space weather, edited by Enrico Camporeale, Simon Wing, Jay R. Johnson, (electronic book)

Machine learning techniques for space weather, edited by Enrico Camporeale, Simon Wing, Jay R. Johnson, (electronic book)

Label
Machine learning techniques for space weather
Title
Machine learning techniques for space weather
Statement of responsibility
edited by Enrico Camporeale, Simon Wing, Jay R. Johnson
Contributor
Subject
Language
eng
Member of
Cataloging source
YDX
Dewey number
629.416
Index
index present
LC call number
TL1489
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Camporeale, Enrico
  • Wing, Simon
  • Johnson, Jay R
http://library.link/vocab/subjectName
  • Space environment
  • Machine learning
Label
Machine learning techniques for space weather, edited by Enrico Camporeale, Simon Wing, Jay R. Johnson, (electronic book)
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Front Cover; Machine Learning Techniques for Space Weather; Copyright; Contents; Contributors; Introduction; Machine Learning and Space Weather; Scope and Structure of the Book; Acknowledgments; References; Part I: Space Weather; Chapter 1: Societal and Economic Importance of Space Weather; 1 What is Space Weather?; 2 Why Now?; 3 Impacts; 3.1 Geomagnetically Induced Currents; 3.2 Global Navigation Satellite Systems; 3.3 Single-Event Effects; 3.4 Other Radio Systems; 3.5 Satellite Drag; 4 Looking to the Future; 5 Summary and Conclusions; Acknowledgments; References
  • Chapter 2: Data Availability and Forecast Products for Space Weather1 Introduction; 2 Data and Models Based on Machine Learning Approaches; 3 Space Weather Agencies; 3.1 Government Agencies; 3.1.1 NOAA's Data and Products; 3.1.2 NASA; 3.1.3 European Space Agency; 3.1.4 The US Air Force Weather Wing; 3.2 Academic Institutions; 3.2.1 Kyoto University, Japan; 3.2.2 Rice University, USA; 3.2.3 Laboratory for Atmospheric and Space Physics, USA; 3.3 Commercial Providers; 3.4 Other Nonprofit, Corporate Research Agencies; 3.4.1 USGS; 3.4.2 JHU Applied Physics Lab
  • 3.4.3 US Naval Research Lab3.4.4 Other International Service Providers; 4 Summary; References; Part II: Machine Learning; Chapter 3: An Information-Theoretical Approach to Space Weather; 1 Introduction; 2 Complex Systems Framework; 3 State Variables; 4 Dependency, Correlations, and Information; 4.1 Mutual Information as a Measure of Nonlinear Dependence; 4.2 Cumulant-Based Cost as a Measure of Nonlinear Dependence; 4.3 Causal Dependence; 4.4 Transfer Entropy and Redundancy as Measures of Causal Relations; 4.5 Conditional Redundancy; 4.6 Significance of Discriminating Statistics
  • 4.7 Mutual Information and Information Flow5 Examples From Magnetospheric Dynamics; 6 Significance as an Indicator of Changes in Underlying Dynamics; 6.1 Detecting Dynamics in a Noisy System; 6.2 Cumulant-Based Information Flow; 7 Discussion; 8 Summary; Acknowledgments; References; Chapter 4: Regression; 1 What is Regression?; 2 Learning From Noisy Data; 2.1 Prediction Errors; 2.2 A Probabilistic Set-Up; 2.3 The Least Squares Method for Linear Regression; 2.3.1 The Least Squares Method and the Best Linear Predictor
  • 2.3.2 The Least Squares Method and the Maximum Likelihood Principle2.3.3 A More General Approach and Higher-Order Predictors; 2.4 Overfitting; 2.4.1 The Order Selection Problem; Error Decomposition: The Bias Versus Variance Trade-Off; Some Popular Order Selection Criteria; 2.4.2 Regularization; 2.5 From Point Predictors to Interval Predictors; 2.5.1 Distribution-Free Interval Predictors; 2.6 Probability Density Estimation; 3 Predictions Without Probabilities; 3.1 Approximation Theory; Dense Sets; Best Approximator; 3.1.1 Neural Networks
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9780128117897
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Specific material designation
remote
System control number
  • on1039305985
  • (OCoLC)1039305985
Label
Machine learning techniques for space weather, edited by Enrico Camporeale, Simon Wing, Jay R. Johnson, (electronic book)
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Front Cover; Machine Learning Techniques for Space Weather; Copyright; Contents; Contributors; Introduction; Machine Learning and Space Weather; Scope and Structure of the Book; Acknowledgments; References; Part I: Space Weather; Chapter 1: Societal and Economic Importance of Space Weather; 1 What is Space Weather?; 2 Why Now?; 3 Impacts; 3.1 Geomagnetically Induced Currents; 3.2 Global Navigation Satellite Systems; 3.3 Single-Event Effects; 3.4 Other Radio Systems; 3.5 Satellite Drag; 4 Looking to the Future; 5 Summary and Conclusions; Acknowledgments; References
  • Chapter 2: Data Availability and Forecast Products for Space Weather1 Introduction; 2 Data and Models Based on Machine Learning Approaches; 3 Space Weather Agencies; 3.1 Government Agencies; 3.1.1 NOAA's Data and Products; 3.1.2 NASA; 3.1.3 European Space Agency; 3.1.4 The US Air Force Weather Wing; 3.2 Academic Institutions; 3.2.1 Kyoto University, Japan; 3.2.2 Rice University, USA; 3.2.3 Laboratory for Atmospheric and Space Physics, USA; 3.3 Commercial Providers; 3.4 Other Nonprofit, Corporate Research Agencies; 3.4.1 USGS; 3.4.2 JHU Applied Physics Lab
  • 3.4.3 US Naval Research Lab3.4.4 Other International Service Providers; 4 Summary; References; Part II: Machine Learning; Chapter 3: An Information-Theoretical Approach to Space Weather; 1 Introduction; 2 Complex Systems Framework; 3 State Variables; 4 Dependency, Correlations, and Information; 4.1 Mutual Information as a Measure of Nonlinear Dependence; 4.2 Cumulant-Based Cost as a Measure of Nonlinear Dependence; 4.3 Causal Dependence; 4.4 Transfer Entropy and Redundancy as Measures of Causal Relations; 4.5 Conditional Redundancy; 4.6 Significance of Discriminating Statistics
  • 4.7 Mutual Information and Information Flow5 Examples From Magnetospheric Dynamics; 6 Significance as an Indicator of Changes in Underlying Dynamics; 6.1 Detecting Dynamics in a Noisy System; 6.2 Cumulant-Based Information Flow; 7 Discussion; 8 Summary; Acknowledgments; References; Chapter 4: Regression; 1 What is Regression?; 2 Learning From Noisy Data; 2.1 Prediction Errors; 2.2 A Probabilistic Set-Up; 2.3 The Least Squares Method for Linear Regression; 2.3.1 The Least Squares Method and the Best Linear Predictor
  • 2.3.2 The Least Squares Method and the Maximum Likelihood Principle2.3.3 A More General Approach and Higher-Order Predictors; 2.4 Overfitting; 2.4.1 The Order Selection Problem; Error Decomposition: The Bias Versus Variance Trade-Off; Some Popular Order Selection Criteria; 2.4.2 Regularization; 2.5 From Point Predictors to Interval Predictors; 2.5.1 Distribution-Free Interval Predictors; 2.6 Probability Density Estimation; 3 Predictions Without Probabilities; 3.1 Approximation Theory; Dense Sets; Best Approximator; 3.1.1 Neural Networks
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9780128117897
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Specific material designation
remote
System control number
  • on1039305985
  • (OCoLC)1039305985

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