Self-learning Procedures for a Kernel Fuzzy Clustering System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F19%3AA2001U0K" target="_blank" >RIV/61988987:17610/19:A2001U0K - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-91008-6_49" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-91008-6_49</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-91008-6" target="_blank" >10.1007/978-3-319-91008-6</a>
Alternative languages
Result language
angličtina
Original language name
Self-learning Procedures for a Kernel Fuzzy Clustering System
Original language description
The paper exemplifies several self-learning methods through the prism of diverse objective functions used for training a kernel fuzzy clustering system. A self-learning process for synaptic weights is implemented in terms of the competitive learning concept and the probabilistic fuzzy clustering approach. The main feature of the introduced fuzzy clustering system is its capability to cluster data in an online way under conditions when clusters are rather likely to be of an arbitrary shape (which cannot usually be separated in a linear manner) and to be mutually intersecting. Generally speaking, the offered system’s topology is mainly based on both the fuzzy clustering neural network by Kohonen and the general regression neural network. When it comes to training this hybrid system, it is grounded on both the lazy and optimizationbased learning concepts.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Computer Science for Engineering and Education
ISBN
978-3-319-91007-9
ISSN
2194-5357
e-ISSN
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Number of pages
11
Pages from-to
487-497
Publisher name
Springer International Publishing AG
Place of publication
Berlin
Event location
Kiev, Ukraine
Event date
Jan 1, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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