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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10102 - Applied mathematics

Result continuities

  • Project

  • 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

  • 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