Scalable Meta-Bayesian Based Hyperparameters Optimization for Machine Learning-Reference-Cited by-同舟云学术

Scalable Meta-Bayesian Based Hyperparameters Optimization for Machine Learning

Published:2022 Issue: Volume: Page:173-186
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
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Author:

Garouani Moncef,Ahmad Adeel,Bouneffa Mourad,Hamlich Mohamed

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-20490-6_14

Reference32 articles.

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2. Muñoz, M.A., Sun, Y., Kirley, M., Halgamuge, S.K.: Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges. Inf. Sci. 317, 224–245 (2015). https://doi.org/10.1016/j.ins.2015.05.010

3. Feurer, M., Letham, B., Hutter, F., Bakshy, E.: Practical transfer learning for Bayesian optimization. arXiv:1802.02219 [cs, stat] (2022). arXiv: 1802.02219

4. Wu, J., et al.: Hyperparameter optimization for machine learning models based on Bayesian optimization. J. Electron. Sci. Technol. 17(1), 26–40 (2019). https://doi.org/10.11989/JEST.1674-862X.80904120

5. Garouani, M., et al.: Towards big industrial data mining through explainable automated machine learning. Int. J. Adv. Manuf. Technol. 120(1), 1169–1188 (2022). https://doi.org/10.1007/s00170-022-08761-9

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