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SOM in Hilbert Space

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00333216" target="_blank" >RIV/68407700:21340/19:00333216 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.14311/NNW.2019.29.002" target="_blank" >https://doi.org/10.14311/NNW.2019.29.002</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.14311/NNW.2019.29.002" target="_blank" >10.14311/NNW.2019.29.002</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    SOM in Hilbert Space

  • Original language description

    The self organization can be performed in an Euclidean space as usually denned or in any metric space which is generalization of previous one. Both approaches have advantages and disadvantages. A novel method of batch SOM learning is designed to yield from the properties of the Hilbert space. This method is able to operate with finite or infinite dimensional patterns from vector space using only their scalar product. The paper is focused on the formulation of objective function and algorithm for its local minimization in a discrete space of partitions. General methodology is demonstrated on pattern sets from a space of functions.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

  • Volume of the periodical

    29

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    13

  • Pages from-to

    19-31

  • UT code for WoS article

    000467936400002

  • EID of the result in the Scopus database

    2-s2.0-85064400630