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Growing neural gas efficiently

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00205386" target="_blank" >RIV/68407700:21230/13:00205386 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.sciencedirect.com/science/article/pii/S0925231212008351" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231212008351</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.neucom.2012.10.004" target="_blank" >10.1016/j.neucom.2012.10.004</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Growing neural gas efficiently

  • Original language description

    This paper presents optimization techniques that substantially speed up the Growing Neural Gas (GNG) algorithm. The GNG is an example of the Self-Organizing Map algorithm that is a subject of an intensive research interest in recent years as it is used in various practical applications. However, a poor time performance on large scale problems requiring neural networks with a high amount of nodes can be a limiting factor for further applications (e.g., cluster analysis, classification, 3-D reconstruction) or a wider usage. We propose two optimization techniques that are aimed exclusively on an efficient implementation of the GNG algorithm internal structure rather than on a modification of the original algorithm. The proposed optimizations preserve allproperties of the GNG algorithm and enable to use it on large scale problems with reduced computational requirements in several orders of magnitude.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2013

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neurocomputing

  • ISSN

    0925-2312

  • e-ISSN

  • Volume of the periodical

    104

  • Issue of the periodical within the volume

    March

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    11

  • Pages from-to

    72-82

  • UT code for WoS article

    000316163500007

  • EID of the result in the Scopus database