Machine Learning in Earth, Environmental and Planetary Sciences
EUR 143.00
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| ISBN13: | 9780443152849 |
| ISBN10: | 0443152845 |
| Binding: | Paperback |
| No. of pages: | 388 pages |
| Size: | 276x215 mm |
| Weight: | 1070 g |
| Language: | English |
| 624 |
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.
- Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data
- Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes
- Includes numerous figures, illustrations and tables to help readers better understand the concepts covered
1. Dataset Preparation 2. Pre-processing approaches 3. Post-processing approaches 4. Non-tuned single-layer feed-forward neural network Learning Machine - Concept 5. Non-tuned single-layer feed-forward neural network Learning Machine - Coding and implementation 6. Outlier-based models of the non-tuned neural network - Concept 7. Outlier-based models of the non-tuned neural network - Coding and implementation 8. Online Sequential non-tuned neural network - Concept 9. Online Sequential non-tuned neural network - Coding and implementation 10. Self-Adaptive Evolutionary of non-tuned neural network - Concept 11. Self-Adaptive Evolutionary of non-tuned neural network - Coding and implementation
