Self-learning Procedures for a Kernel Fuzzy Clustering System

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-learning Procedures for a Kernel Fuzzy Clustering System

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Self-learning Procedures for a Kernel Fuzzy Clustering System

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Computer Science for Engineering and Education

ISBN

978-3-319-91007-9

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

487-497

Název nakladatele

Springer International Publishing AG

Místo vydání

Berlin

Místo konání akce

Kiev, Ukraine

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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