Utvidet returrett til 31. januar 2025

Machine Learning in Earth, Environmental and Planetary Sciences

Om 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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  • Språk:
  • Ukjent
  • ISBN:
  • 9780443152849
  • Bindende:
  • Paperback
  • Sider:
  • 388
  • Utgitt:
  • 27. juni 2023
  • Dimensjoner:
  • 276x24x213 mm.
  • Vekt:
  • 1066 g.
  • BLACK NOVEMBER
  På lager
Leveringstid: 4-7 virkedager
Forventet levering: 4. desember 2024

Beskrivelse av 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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