
Self-Learning and Adaptive Algorithms for Business Applications
Description
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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.
Reviews / Votes
This guide explains how to apply methods using systems built by a combination of the neural network approach and fuzzy logic (neuro-fuzzy systems) to solve practical data classification problems in business. It describes methods aimed at handling the main types of uncertainties in data, using adaptive methods of fuzzy clustering; the use of Kohonen maps and their ensembles for fuzzy clustering tasks; and simulation results of these neuro-fuzzy architectures, their learning methods, self-organization, and clustering procedures. -- Annotation (c)2019 * (protoview.com) *More details
Persons
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.
Content
2. Adaptive Methods of Fuzzy Clustering
3. Kohonen Maps and their Ensembles for Fuzzy Clustering Tasks
4. Simulation Results and Solutions for Practical Tasks
Conclusion
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