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)
Resource Information
The item Machine learning techniques for space weather, edited by Enrico Camporeale, Simon Wing, Jay R. Johnson, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Machine learning techniques for space weather, edited by Enrico Camporeale, Simon Wing, Jay R. Johnson, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.
This item is available to borrow from 1 library branch.
- Language
- eng
- Extent
- 1 online resource.
- 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
- Isbn
- 9780128117897
- 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
- Language
- eng
- 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)
- 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)
- 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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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Machine-learning-techniques-for-space-weather/2sxxP5iqxXM/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Machine-learning-techniques-for-space-weather/2sxxP5iqxXM/">Machine learning techniques for space weather, edited by Enrico Camporeale, Simon Wing, Jay R. Johnson, (electronic book)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">University of Liverpool</a></span></span></span></span></div>
