| Description |
1 online resource |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
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 |
| Note |
8.5 Introduction to the online sequential extreme learning machine |
|
Description based on online resource; title from digital title page (viewed on August 15, 2023). |
| Subject |
Earth sciences -- Data processing.
|
|
Environmental sciences -- Data processing.
|
|
Planetary science -- Data processing.
|
|
Machine learning.
|
|
Sciences de l'environnement -- Informatique.
|
|
Planétologie -- Informatique.
|
|
Apprentissage automatique.
|
|
Earth sciences -- Data processing.
(OCoLC)fst00900738
|
|
Machine learning. (OCoLC)fst01004795
|
| Added Author |
Ebtehaj, Isa, author.
|
|
Ladouceur, Joseph D., author.
|
| Other Form: |
Print version: Bonakdari, Hossein Machine Learning in Earth, Environmental and Planetary Sciences San Diego : Elsevier,c2023 9780443152849 |
| ISBN |
0443152853 electronic book |
|
9780443152856 (electronic bk.) |
|
9780443152849 |
|
0443152845 |
| Standard No. |
AU@ 000074865421 |
|
