Optimization of Neural Network Training for Wine Quality Classification Using Simulated Annealing

Mingfei Duan

doi:10.1051/shsconf/202214403012

All issues

Volume 144 (2022)

SHS Web Conf., 144 (2022) 03012

References

Open Access

Issue		SHS Web Conf. Volume 144, 2022 2022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)


Article Number		03012
Number of page(s)		4
Section		Application of Artificial Intelligence Technology and Machine Learning Algorithms
DOI		https://doi.org/10.1051/shsconf/202214403012
Published online		26 August 2022

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[1] Rumelhart, D., Hinton, G. & Williams, R. Learning representations by back-propagating errors. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0 [Google Scholar]

[2] S. Chalup and F. Maire, “A Study on Hill Climbing Algorithms for Neural Network Training”, Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99) July 6–9 1999, vol. 3, pp. 2014-2021, 1999. [Google Scholar]

[3] P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009. https://archive.ics.uci.edu/ml/datasets/wine+quality [CrossRef] [Google Scholar]

[4] Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375. [Google Scholar]

[5] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller and E. Teller, “Equation of state calculations by fast computing machines” in J. of Chem. Phys., vol. 21, no. 6, pp. 1087-1092, 1953. [CrossRef] [Google Scholar]

[6] Taha, A.A., Hanbury, A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15, 29 (2015). https://doi.org/10.1186/s12880-015-0068-x [CrossRef] [Google Scholar]