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Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions /
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.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Bingley, UK :
Emerald Publishing,
2019.
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| Edición: | First edition. |
| Colección: | Emerald points.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 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 by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Bingley, UK : |b Emerald Publishing, |c 2019. | |
| 300 | |a 1 online resource | ||
| 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. | ||
| 588 | |a Online resource; title from PDF title page (EBSCO, viewed June 13, 2019). | ||
| 505 | 0 | |a Front Cover; Self-Learning and Adaptive Algorithms for Business Applications; Copyright Page; Contents; Acknowledgment; Introduction; Chapter 1 Review of the Problem Area; 1.1. Learning and Self-learning Procedures; 1.2. Clustering; 1.2.1. Clustering Methods; 1.3. Fuzzy Sets and Fuzzy Logic; 1.3.1. Fuzzy Inference Systems and Fuzzy Control; 1.3.2. Type-2 Fuzzy Logic; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning | |
| 505 | 8 | |a 1.4.3. Recurrent Neural Networks1.5. Neuro-fuzzy Systems; Chapter 2 Adaptive Methods of Fuzzy Clustering; 2.1. An Objective Function for Fuzzy Clustering; 2.2. Optimization of the Objective Function; 2.3. A Linear Variable Fuzzifier; 2.3.1. Adaptive Fuzzy Clustering with a Variable Fuzzifier; 2.3.2. Possibilistic Fuzzy Clustering with a Variable Fuzzifier; 2.3.3. A Suppression Procedure for Fuzzy Clustering; 2.4. Methods Based on the Gustafson-Kessel Procedure; 2.4.1. The Basic Gustafson-Kessel Method; 2.4.2. A Possibilistic Version of the Gustafson-Kessel Method | |
| 505 | 8 | |a 2.4.3. Adaptive Versions of the Gustafson-Kessel Algorithm2.5. A Robust Fuzzy Clustering Method Based on the Cauchy Criterion; 2.5.1. The Probabilistic Approach; 2.5.2. The Possibilistic Approach; Chapter 3 Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks; 3.1. The Competitive Learning; 3.2. Kohonen Neural Networks; 3.3. Modifications of Kohonen Self-organizing Maps; 3.4. Ensembles and Their Learning Methods; 3.4.1. Reasons for Using Ensembles; 3.4.2. Basic Notions of the Theory of Collective Output Systems; 3.4.2.1. Confidence; 3.4.2.2. Diversification | |
| 505 | 8 | |a 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. Complex Systems of the Collective Output; 3.5. Ensembles of Neuro-fuzzy Kohonen Networks; 3.6. Fuzzy Type-2 Clustering Using Ensembles of Modified Neuro-fuzzy Kohonen Networks; Chapter 4 Simulation Results and Solutions for Practical Tasks; 4.1. Simulation of the Adaptive Neuro-fuzzy Kohonen Network with a Variable Fuzzifier; 4.1.1. Comparative Efficiency; 4.1.2. The Fuzzifier's Influence; 4.1.3. Influence of the Suppression Parameter | |
| 505 | 8 | |a 4.2. Simulation of Adaptive Versions the Gustafson-Kessel Algorithm4.3. Simulation of the Robust Clustering Method Based on the Cauchy Criterion; 4.4. Solving the Task of Automated Cataloging of Illustrative Materials; Conclusion; References | |
| 520 | |a 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. | ||
| 590 | |a Emerald Insight |b Emerald All Book Titles | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Electronic data processing. | |
| 650 | 0 | |a Business |x Data processing. | |
| 650 | 0 | |a Fuzzy systems. | |
| 650 | 6 | |a Gestion |x Informatique. | |
| 650 | 6 | |a Systèmes flous. | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Industrial Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management Science. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Organizational Behavior. |2 bisacsh | |
| 650 | 7 | |a Business |x Data processing |2 fast | |
| 650 | 7 | |a Electronic data processing |2 fast | |
| 650 | 7 | |a Fuzzy systems |2 fast | |
| 700 | 1 | |a Bodyanskiy, Yevgeniy V., |e author | |
| 700 | 1 | |a Tyshchenko, Oleksii, |e author. | |
| 758 | |i has work: |a Self-learning and adaptive algorithms for business applications (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFTv6ppQ4kHBCXGPcDkfYP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version : |z 9781838671747 |
| 830 | 0 | |a Emerald points. | |
| 856 | 4 | 0 | |u https://www.emerald.com/insight/publication/doi/10.1108/9781838671716 |z Texto completo |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5787820 |z Texto completo |
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