Machine Learning in Earth, Environmental and Planetary Sciences : Theoretical and Practical Applications /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Bonakdari, Hossein (Autor), Ebtehaj, Isa (Autor), Ladouceur, Joseph D. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Cambridge, MA : Elsevier, [2023]
Temas:
Earth sciences > Data processing.
Environmental sciences > Data processing.
Planetary science > Data processing.
Machine learning.
Sciences de l'environnement > Informatique.
Plan�etologie > Informatique.
Apprentissage automatique.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • About the authors
  • Preface
  • Acknowledgments
  • About the cover image
  • Chapter 1. Dataset preparation
  • Abstract
  • 1.1 The modeling process
  • 1.2 Data description
  • 1.3 Different types of problems
  • 1.4 Summary
  • Appendix 1A Supporting information
  • Appendix 1A Supplementary information
  • References
  • Chapter 2. Preprocessing approaches
  • Abstract
  • 2.1 Normalization
  • 2.2 Standardization
  • 2.3 Data splitting
  • 2.4 Cross-validation
  • 2.5 Summary
  • Appendix 2A Supporting information
  • Appendix 2A Supplementary information
  • References
  • Chapter 3. Postprocessing approaches
  • Abstract
  • 3.1 Introduction
  • 3.2 Quantitative tools
  • 3.3 Qualitative tools
  • 3.4 Summary
  • Appendix 3A Supporting information
  • Appendix 3A Supplementary information
  • References
  • Chapter 4. Non-tuned single-layer feed-forward neural network learning machine-concept
  • Abstract
  • 4.1 Machine learning application in applied science
  • 4.2 Mathematical definition of extreme learning machine model
  • 4.3 Activation function in the extreme learning machine model
  • 4.4 Summary
  • References
  • Chapter 5. Non-tuned single-layer feed-forward neural network learning machine-coding and implementation
  • Abstract
  • 5.1 Introduction
  • 5.2 Extreme learning machine implementation in the MATLAB environment
  • 5.3 Extreme learning machine modeling output
  • 5.4 Calculator for extreme learning machine model
  • 5.5 Effect of the extreme learning machine parameters
  • 5.6 The effect of hidden layer neurons on Example 5
  • 5.7 Summary
  • Appendix 5.A Supporting information
  • Appendix 5.A Supporting information
  • References
  • Chapter 6. Outlier-based models of the non-tuned neural network-concept
  • Abstract
  • 6.1 Background of extreme learning machines
  • 6.2 Extreme learning machine in the presence of outliers
  • 6.3 Mathematical definition of extreme learning machine-based models
  • 6.4 Summary
  • References
  • Chapter 7. Outlier-based models of the non-tuned neural network-coding and implementation
  • Abstract
  • 7.1 Developed extreme learning machine-based approaches in the presence of outliers
  • 7.2 Implementation of the developed extreme learning machine-based models in the MATLAB
  • 7.3 Calculator for outlier-based extreme learning machine models
  • 7.4 Evaluating the effects of user-defined parameters on the modeling results of the extreme learning machine-based models
  • 7.5 Summary
  • Appendix 7.A Supporting information
  • References
  • Chapter 8. Online sequential non-tuned neural network-concept
  • Abstract
  • 8.1 Introduction
  • 8.2 Main architectures of the single-layer feed-forward neural network
  • 8.3 Development of the sequential-based learning algorithm
  • 8.4 Main drawbacks of the classical sequential-based learning algorithms

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