A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

Author:

Wang Xuan,Dong Yan,Yang Jing,Liu Zhipeng,Lu Jinsuo

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science

Reference50 articles.

1. Afzaal H, Farooque A A, Abbas F, Acharya B, Esau T (2020). Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water, 12(1): 5

2. Audet C, Kokkolaras M (2016). Blackbox and derivative-free optimization: Theory, algorithms and applications. Optimization and Engineering, 17(1): 1–2

3. Bergstra J, Bardenet R, Bengio Y, Kégl B (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Granada, December 12–15, 2011

4. Bergstra J, Bengio Y (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13: 281–305

5. Bergstra J, Yamins D, Cox D (2013). Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference. Scipy, Austin, June 24–29, 2013

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