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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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