Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions

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,...

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Bibliographic Details
Main Authors: Hu, Zhengbing, (Author), Bodyanskiy, Yevgeniy V., (Author), Tyshchenko, Oleksii, (Author)
Format: eBook
Language: English
Published: Bingley, U.K. : Emerald Publishing Limited, 2019.
Series: Emerald points.
Subjects:
Business > Data processing.
Electronic data processing.
Fuzzy systems.
Business & Economics > Research & Development.
Neural networks & fuzzy systems.
elektronické knihy
electronic books
ISBN: 9781838671716 (e-book)
Physical Description: 1 online resource (vii, 111 pages) ; cm.

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Table of contents

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020 |a 9781838671716 (e-book) 
040 |a UtOrBLW  |b eng  |e rda  |c UtOrBLW 
080 |a 658 
100 1 |a Hu, Zhengbing,  |e author. 
245 1 0 |a Self-learning and adaptive algorithms for business applications :  |b a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions /  |c Zhengbing Hu, Yevgeniy V. Bodyanskiy, and Oleksii K. Tyshchenko. 
264 1 |a Bingley, U.K. :  |b Emerald Publishing Limited,  |c 2019. 
264 4 |c ©2019 
300 |a 1 online resource (vii, 111 pages) ;  |c cm. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Emerald points 
504 |a Includes bibliographical references. 
505 0 |a Prelims -- Introduction -- Review of the problem area -- Adaptive methods of fuzzy clustering -- Kohonen maps and their ensembles for fuzzy clustering tasks -- Simulation results and solutions for practical tasks -- Conclusion -- References. 
520 |a 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.  
588 0 |a Print version record. 
650 0 |a Business  |x Data processing. 
650 0 |a Electronic data processing. 
650 0 |a Fuzzy systems. 
650 7 |a Business & Economics  |x Research & Development.  |2 bisacsh 
650 7 |a Neural networks & fuzzy systems.  |2 bicssc 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Bodyanskiy, Yevgeniy V.,  |e author. 
700 1 |a Tyshchenko, Oleksii,  |e author. 
776 |z 9781838671747 
830 0 |a Emerald points. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://doi.org/10.1108/9781838671716  |y Full text 

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