Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation.
This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results.
1. Machine Learning in Earth, Environmental and Planetary Sciences - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. About the authors - p. xi 8. Preface - p. xiii 9. Acknowledgments - p. xv 10. About the cover image - p. xvii 11. Chapter 1 Dataset preparation - p. 1 12. Chapter 2 Preprocessing approaches - p. 49 13. Chapter 3 Postprocessing approaches - p. 85 14. Chapter 4 Non-tuned single-layer feed-forward neural network learning machine—concept - p. 151 15. Chapter 5 Non-tuned single-layer feed-forward neural network learning machine—coding and implementation - p. 165 16. Chapter 6 Outlier-based models of the non-tuned neural network—concept - p. 201 17. Chapter 7 Outlier-based models of the non-tuned neural network—coding and implementation - p. 215 18. Chapter 8 Online sequential non-tuned neural network—concept - p. 255 19. Chapter 9 Online sequential nontuned neural network—coding and implementation - p. 273 20. Chapter 10 Self-adaptive evolutionary of non-tuned neural network—concept - p. 307 21. Chapter 11 Self-adaptive evolutionary of non-tuned neural network—coding and implementation - p. 325 22. Index - p. 367
Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation.
This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results.
1. Machine Learning in Earth, Environmental and Planetary Sciences - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. About the authors - p. xi 8. Preface - p. xiii 9. Acknowledgments - p. xv 10. About the cover image - p. xvii 11. Chapter 1 Dataset preparation - p. 1 12. Chapter 2 Preprocessing approaches - p. 49 13. Chapter 3 Postprocessing approaches - p. 85 14. Chapter 4 Non-tuned single-layer feed-forward neural network learning machine—concept - p. 151 15. Chapter 5 Non-tuned single-layer feed-forward neural network learning machine—coding and implementation - p. 165 16. Chapter 6 Outlier-based models of the non-tuned neural network—concept - p. 201 17. Chapter 7 Outlier-based models of the non-tuned neural network—coding and implementation - p. 215 18. Chapter 8 Online sequential non-tuned neural network—concept - p. 255 19. Chapter 9 Online sequential nontuned neural network—coding and implementation - p. 273 20. Chapter 10 Self-adaptive evolutionary of non-tuned neural network—concept - p. 307 21. Chapter 11 Self-adaptive evolutionary of non-tuned neural network—coding and implementation - p. 325 22. Index - p. 367
