Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology
Journal Title: Informatics - Year 2019, Vol 6, Issue 1
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.
Authors and Affiliations
Ioannis E. Livieris
Keywords
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- EP ID EP44175
- DOI https://doi.org/10.3390/informatics6010001
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