Boosting of Neural Networks over MNIST Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F14%3AA1501BXG" target="_blank" >RIV/61988987:17310/14:A1501BXG - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Boosting of Neural Networks over MNIST Data

Popis výsledku v původním jazyce

The methods proposed in the article come out from a technique called boosting, which is based on the principle of combining a large number of so-called weak classifiers into a strong classifier. The article is focused on the possibility of increasing theefficiency of the algorithms via their appropriate combination, and particularly increasing their reliability and reducing their time exigency. Time exigency does not mean time exigency of the algorithm itself, nor its development, but time exigency ofapplying the algorithm to a particular problem domain. Simulations and experiments of the proposed processes were performed in the designed and created application environment. Experiments have been conducted over the MNIST database of handwritten digitsthat is commonly used for training and testing in the field of machine learning. Finally, a comparative experimental study with other approaches is presented. All achieved results are summarized in a conclusion.

Název v anglickém jazyce

Boosting of Neural Networks over MNIST Data

Popis výsledku anglicky

The methods proposed in the article come out from a technique called boosting, which is based on the principle of combining a large number of so-called weak classifiers into a strong classifier. The article is focused on the possibility of increasing theefficiency of the algorithms via their appropriate combination, and particularly increasing their reliability and reducing their time exigency. Time exigency does not mean time exigency of the algorithm itself, nor its development, but time exigency ofapplying the algorithm to a particular problem domain. Simulations and experiments of the proposed processes were performed in the designed and created application environment. Experiments have been conducted over the MNIST database of handwritten digitsthat is commonly used for training and testing in the field of machine learning. Finally, a comparative experimental study with other approaches is presented. All achieved results are summarized in a conclusion.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the International Conference on Neural Computation Theory and Applications

ISBN

978-989-758-054-3

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

256-263

Název nakladatele

INSTICC Press

Místo vydání

Portugal

Místo konání akce

Rome, Italy

Datum konání akce

22. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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