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,...
Gespeichert in:
| Beteilige Person: | |
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| Weitere beteiligte Personen: | , |
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Bingley, U.K.
Emerald Publishing Limited
2019
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| Schriftenreihe: | Emerald points
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| Links: | https://doi.org/10.1108/9781838671716 |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (vii, 111 Seiten) |
| ISBN: | 9781838671716 (e-book) |
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| spelling | Hu, Zhengbing Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions Zhengbing Hu, Yevgeniy V. Bodyanskiy, and Oleksii K. Tyshchenko Bingley, U.K. Emerald Publishing Limited 2019 ©2019 1 Online-Ressource (vii, 111 Seiten) txt c cr Emerald points 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. Bodyanskiy, Yevgeniy V. Tyshchenko, Oleksii Erscheint auch als Druck-Ausgabe 9781838671747 |
| spellingShingle | Hu, Zhengbing Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| title | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_auth | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_exact_search | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_full | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions Zhengbing Hu, Yevgeniy V. Bodyanskiy, and Oleksii K. Tyshchenko |
| title_fullStr | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions Zhengbing Hu, Yevgeniy V. Bodyanskiy, and Oleksii K. Tyshchenko |
| title_full_unstemmed | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions Zhengbing Hu, Yevgeniy V. Bodyanskiy, and Oleksii K. Tyshchenko |
| title_short | Self-learning and adaptive algorithms for business applications |
| title_sort | self learning and adaptive algorithms for business applications a guide to adaptive neuro fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_sub | a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
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