Comparative Study of Surrogate Techniques for CNN Hyperparameter Optimization
Optimizing hyper parameters in Convolutional Neural networks is a tedious process for many researchers and practitioners. It requires a high degree of expertise or experience to optimise the hyper parameters, and manual optimisation is likely to be biased. To date, methods or approaches to automate...
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| 主要な著者: | , , |
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| フォーマット: | Book Section |
| 言語: | English |
| 出版事項: |
Computing & Intelligent Systems, SCRS
2022
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| オンライン･アクセス: | http://utpedia.utp.edu.my/id/eprint/24082/1/Comparative%20Study%20of%20Surrogate%20Techniques%20for%20CNN%20Hyperparameter%20Optimization.pdf https://doi.org/10.52458/978-81-95502-00-4-48 http://utpedia.utp.edu.my/id/eprint/24082/ https://www.publications.scrs.in/chapter/978-81-95502-00-4/48 https://doi.org/10.52458/978-81-95502-00-4-48 |
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http://utpedia.utp.edu.my/id/eprint/24082/1/Comparative%20Study%20of%20Surrogate%20Techniques%20for%20CNN%20Hyperparameter%20Optimization.pdfhttps://doi.org/10.52458/978-81-95502-00-4-48
http://utpedia.utp.edu.my/id/eprint/24082/
https://www.publications.scrs.in/chapter/978-81-95502-00-4/48
https://doi.org/10.52458/978-81-95502-00-4-48