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Informatics 2019, 6(1), 1; https://doi.org/10.3390/informatics6010001

Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology

Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, GR 263-34 Antirion, Greece
Received: 31 October 2018 / Revised: 10 December 2018 / Accepted: 24 December 2018 / Published: 28 December 2018
(This article belongs to the Special Issue Advances in Randomized Neural Networks)
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Abstract

In this work, a new approach for training artificial neural networks is presented which utilises techniques for solving the constraint optimisation problem. More specifically, this study converts the training of a neural network into a constraint optimisation problem. Furthermore, we propose a new neural network training algorithm based on the L-BFGS-B method. Our numerical experiments illustrate the classification efficiency of the proposed algorithm and of our proposed methodology, leading to more efficient, stable and robust predictive models. View Full-Text
Keywords: artificial neural networks; constrained optimisation; L-BFGS-B; accuracy artificial neural networks; constrained optimisation; L-BFGS-B; accuracy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Livieris, I.E. Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology. Informatics 2019, 6, 1.

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