Advanced Machine Learning Techniques includes the theoretical
foundations of modern machine learning, as well as advanced methods
and frameworks used in modern machine learning. Handbook of
HydroInformatics, Volume II: Advanced Machine Learning Techniques
presents both the art of designing good learning algorithms, as
well as the science of analyzing an algorithm's computational and
statistical properties and performance guarantees. The global
contributors cover theoretical foundational topics such as
computational and statistical convergence rates, minimax
estimation, and concentration of measure as well as advanced
machine learning methods, such as nonparametric density estimation,
nonparametric regression, and Bayesian estimation; additionally,
advanced frameworks such as privacy, causality, and stochastic
learning algorithms are also included. Lastly, the volume presents
Cloud and Cluster Computing, Data Fusion Techniques, Empirical
Orthogonal Functions and Teleconnection, Internet of Things,
Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition,
Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse
Mode. This is an interdisciplinary book, and the audience includes
postgraduates and early-career researchers interested in: Computer
Science, Mathematical Science, Applied Science, Earth and
Geoscience, Geography, Civil Engineering, Engineering, Water
Science, Atmospheric Science, Social Science, Environment Science,
Natural Resources, Chemical Engineering.
General
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