Boosting of Neural Networks over MNIST Data

Eva Volna, Vaclav Kocian, Martin Kotyrba

Abstract

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 the efficiency 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 of applying 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 digits that 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.

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Paper Citation


in Harvard Style

Volna E., Kocian V. and Kotyrba M. (2014). Boosting of Neural Networks over MNIST Data . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 256-263. DOI: 10.5220/0005131802560263


in Bibtex Style

@conference{ncta14,
author={Eva Volna and Vaclav Kocian and Martin Kotyrba},
title={Boosting of Neural Networks over MNIST Data},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={256-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005131802560263},
isbn={978-989-758-054-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Boosting of Neural Networks over MNIST Data
SN - 978-989-758-054-3
AU - Volna E.
AU - Kocian V.
AU - Kotyrba M.
PY - 2014
SP - 256
EP - 263
DO - 10.5220/0005131802560263