Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology
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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.
2014 ◽
Vol 10
(S306)
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pp. 279-287
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2019 ◽
Vol 34
(2)
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pp. 125-135
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2017 ◽
Vol 109
(1)
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pp. 29-38
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2019 ◽
Vol 32
(9)
◽
pp. 4177-4185
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2005 ◽
Vol 12
(3)
◽
pp. 297-305
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