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.
Material type:
TextSeries: Emerald pointsPublisher: Emerald Publishing Limited, Description: 1 online resource (vii, 111 pages) ; cmISBN: 9781838671716 (e-book)Subject(s): Business -- Data processing | Electronic data processing | Fuzzy systems | Business & Economics -- Research & Development | Neural networks & fuzzy systemsAdditional physical formats: No titleDDC classification: 658.054 LOC classification: HF5548.2 | .H89 2019Online resources: Click here to access online | Item type | Current library | Call number | URL | Status | Date due | Barcode | Item holds |
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eBook
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| HF5472.U6 T36 2003 Public Markets and Civic Culture in Nineteenth-Century America | HF5475.C6 K43 2012 Market platforms, industrial clusters and small business dynamics | HF5548 .H387 2009 Punched-Card Systems and the Early Information Explosion, 1880-1945 | HF5548.2 .H89 2019 Self-learning and adaptive algorithms for business applications : | HF5548.32 .E26 2002 The economics of the Internet and E-commerce | HF5548.32 .O745 2003 Organizing the new industrial economy | HF5548.32 W38 2013 Electronic commerce : |
Includes bibliographical references.
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.
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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