Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques: 2-电子书百科大全

Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques: 2 book cover

Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques: 2

Author(s): Eslamian

Publisher: Elsevier
Publication Date: 9 Dec. 2022
Edition: 1st
Language: English
Print length: 418 pages
ISBN-10: 0128219610
ISBN-13: 9780128219614

Book Description

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.

Key insights from 24 contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc.
Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison.
Defines both the designing of good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees.

Editorial Reviews

Review

A fully comprehensive handbook that provides all the information needed on Advanced Machine Learning Techniques

From the Back Cover

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 foundation topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure. Advanced machine learning methods such as nonparametric density estimation, nonparametric regression, and Bayesian estimation, as well as advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Many other methods such as 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, Volume-Based Inverse Mode are included.

This volume is a true interdisciplinary work and the audiences include post graduates and above 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, and Chemical Engineering.

View on Amazon

{"@context":"https://schema.org","@type":"Book","name":"Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques: 2","image":"https://m.media-amazon.com/images/I/41OfNnwz4PL._SX342_SY445_ML2_.jpg","author":{"@type":"Person","name":"Eslamian"},"publisher":{"@type":"Organization","name":"Elsevier"},"datePublished":"9 Dec. 2022","isbn":"9780128219614","numberOfPages":418,"inLanguage":"English","description":"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.Key insights from 24 contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison.Defines both the designing of good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees.","bookEdition":"1st","url":"https://www.amazon.co.uk/dp/0128219610/","bookFormat":"http://schema.org/EBook","additionalType":"http://schema.org/PDF","fileSize":"10 MB","accessibilityFeature":["login required","member access only"],"accessibilitySummary":"PDF version available to authenticated members only. File size: 10 MB."}

代发服务PDF电子书30元立即求助