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

Livieris, I.E. Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology. Informatics 2019, 6, 1. https://doi.org/10.3390/informatics6010001

AMA Style

Livieris IE. Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology. Informatics. 2019; 6(1):1. https://doi.org/10.3390/informatics6010001

Chicago/Turabian Style

Livieris, Ioannis E. 2019. "Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology" Informatics 6, no. 1: 1. https://doi.org/10.3390/informatics6010001

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