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LEADER 00000cam a2200781Ii 4500 
001    on1104209023 
003    OCoLC 
005    20210702123300.7 
006    m     o  d         
007    cr cnu|||unuuu 
008    190611s2019    enk     ob    000 0 eng d 
015    GBB964754|2bnb 
016 7  019349566|2Uk 
020    9781838671716|q(electronic book) 
020    1838671714|q(electronic book) 
020    9781838671730|q(ePub ebook) 
020    1838671730 
020    |z9781838671747 
035    (OCoLC)1104209023 
037    9781838671730|bEmerald Publishing 
040    N$T|beng|erda|epn|cN$T|dN$T|dEBLCP|dOCLCF|dUKMGB|dUKAHL
       |dOTZ|dOCLCQ|dSFB|dOCLCQ|dK6U|dOCLCO 
049    RIDW 
050  4 HD30.2 
072  7 BUS|x082000|2bisacsh 
072  7 BUS|x041000|2bisacsh 
072  7 BUS|x042000|2bisacsh 
072  7 BUS|x085000|2bisacsh 
082 04 658.4038|223 
090    HD30.2 
100 1  Hu, Zhengbing,|0https://id.loc.gov/authorities/names/
       no2009129726|eauthor. 
245 10 Self-learning and adaptive algorithms for business 
       applications :|ba guide to adaptive neuro-fuzzy systems 
       for fuzzy clustering under uncertainty conditions /|cby 
       Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. 
       Tyshchenko. 
250    First edition. 
264  1 Bingley, UK :|bEmerald Publishing,|c2019. 
300    1 online resource. 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
340    |gpolychrome|2rdacc 
347    text file|2rdaft 
490 1  Emerald points 
504    Includes bibliographical references. 
505 0  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  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  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  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  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    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. 
588    Online resource; title from PDF title page (EBSCO, viewed 
       June 13, 2019). 
590    eBooks on EBSCOhost|bEBSCO eBook Subscription Academic 
       Collection - North America 
650  0 Electronic data processing.|0https://id.loc.gov/
       authorities/subjects/sh85042288 
650  0 Business|xData processing.|0https://id.loc.gov/authorities
       /subjects/sh85018264 
650  0 Fuzzy systems.|0https://id.loc.gov/authorities/subjects/
       sh85052628 
650  7 Electronic data processing.|2fast|0https://id.worldcat.org
       /fast/906956 
650  7 Business|xData processing.|2fast|0https://id.worldcat.org/
       fast/842293 
650  7 Fuzzy systems.|2fast|0https://id.worldcat.org/fast/936814 
655  0 Electronic books. 
655  4 Electronic books. 
700 1  Bodyanskiy, Yevgeniy V.,|eauthor. 
700 1  Tyshchenko, Oleksii,|eauthor. 
776 08 |iPrint version :|z9781838671747 
830  0 Emerald points.|0https://id.loc.gov/authorities/names/
       no2018010552 
856 40 |uhttps://rider.idm.oclc.org/login?url=https://
       search.ebscohost.com/login.aspx?direct=true&scope=site&
       db=nlebk&AN=2040570|zOnline ebook via EBSCO. Access 
       restricted to current Rider University students, faculty, 
       and staff. 
856 42 |3Instructions for reading/downloading the EBSCO version 
       of this ebook|uhttp://guides.rider.edu/ebooks/ebsco 
901    MARCIVE 20231220 
948    |d20210708|cEBSCO|tEBSCOebooksacademic NEW 5016 |lridw 
994    92|bRID