Self-Learning and Adaptive Algorithms for Business Applications: A Guide to Adaptive Neuro-Fuzzy Systems for Fuzzy Clustering Under Uncertainty Conditions (Emerald Points) - Softcover
Book 49 of 263: Emerald Points
Synopsis
In today’s data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications.
In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning.
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About the Author
Zhengbing Hu is an Associate Professor, School of Educational Information Technology, Huazhong Normal University, China.
Yevgeniy V. Bodyanskiy is a Professor at the Department of Artificial Intelligence, Kharkiv National University of Radioelectronics.
Oleksii Tyshchenko is a Researcher at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic.
"About this title" may belong to another edition of this title.
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Self-Learning and Adaptive Algorithms for Business Applications
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Paperback. Condition: New. In today's data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications. In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning. Seller Inventory # LU-9781838671747