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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:
Acceso en línea:Texto completo

MARC

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040 |a EBLCP  |b eng  |e rda  |c EBLCP  |d YDX  |d OPELS  |d YDX  |d OCLCO  |d OCLCF 
019 |a 1389484215 
020 |a 9780443152856  |q electronic book 
020 |a 0443152853  |q electronic book 
020 |z 9780443152849 
020 |z 0443152845 
035 |a (OCoLC)1389614675  |z (OCoLC)1389484215 
050 4 |a QE48.8  |b .B66 2023 
082 0 4 |a 550.0285/57  |2 23/eng/20230815 
100 1 |a Bonakdari, Hossein,  |e author. 
245 1 0 |a Machine Learning in Earth, Environmental and Planetary Sciences :  |b Theoretical and Practical Applications /  |c Hossein Bonakdari, Isa Ebtehaj, Joseph D. Ladouceur. 
264 1 |a Amsterdam ;  |a Cambridge, MA :  |b Elsevier,  |c [2023] 
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 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
500 |a 8.5 Introduction to the online sequential extreme learning machine 
588 |a Description based on online resource; title from digital title page (viewed on August 15, 2023). 
650 0 |a Earth sciences  |x Data processing. 
650 0 |a Environmental sciences  |x Data processing. 
650 0 |a Planetary science  |x Data processing. 
650 0 |a Machine learning. 
650 6 |a Sciences de l'environnement  |0 (CaQQLa)201-0097373  |x Informatique.  |0 (CaQQLa)201-0380011 
650 6 |a Plan�etologie  |0 (CaQQLa)201-0138968  |x Informatique.  |0 (CaQQLa)201-0380011 
650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
650 7 |a Earth sciences  |x Data processing.  |2 fast  |0 (OCoLC)fst00900738 
650 7 |a Machine learning.  |2 fast  |0 (OCoLC)fst01004795 
700 1 |a Ebtehaj, Isa,  |e author. 
700 1 |a Ladouceur, Joseph D.,  |e author. 
776 0 8 |i Print version:  |a Bonakdari, Hossein  |t Machine Learning in Earth, Environmental and Planetary Sciences  |d San Diego : Elsevier,c2023  |z 9780443152849 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780443152849  |z Texto completo