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Machine Learning in Earth, Environmental and Planetary Sciences

About Machine Learning in Earth, Environmental and Planetary Sciences

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.

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  • Language:
  • Unknown
  • ISBN:
  • 9780443152849
  • Binding:
  • Paperback
  • Pages:
  • 388
  • Published:
  • June 27, 2023
  • Dimensions:
  • 276x24x213 mm.
  • Weight:
  • 1066 g.
  In stock
Delivery: 3-5 business days
Expected delivery: September 27, 2024

Description of Machine Learning in Earth, Environmental and Planetary Sciences

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.

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