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Machine learning techniques for space weather /
"A thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume d...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Machine learning techniques for space weather / |c edited by Enrico Camporeale, Simon Wing, Jay R. Johnson. |
| 260 | |a Amsterdam, Netherlands : |b Elsevier, |c [2018] | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed June 11, 2018). | |
| 520 | |a "A thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields"--Page 4 of cover | ||
| 650 | 0 | |a Space environment. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Environnement spatial. |0 (CaQQLa)201-0016584 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Space environment |2 fast |0 (OCoLC)fst01127673 | |
| 700 | 1 | |a Camporeale, Enrico. | |
| 700 | 1 | |a Wing, Simon. | |
| 700 | 1 | |a Johnson, Jay R. | |
| 776 | 0 | 8 | |i Print version: |t Machine learning techniques for space weather. |d Amsterdam, Netherlands : Elsevier, [2018] |z 0128117885 |z 9780128117880 |w (OCoLC)1010506041 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128117880 |z Texto completo |