Growing neural gas efficiently

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Growing neural gas efficiently

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Growing neural gas efficiently

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

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Svazek periodika

104

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

72-82

Kód UT WoS článku

000316163500007

EID výsledku v databázi Scopus

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