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Zitong Huang (talk | contribs) Add reference for AutoML |
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* The ''performance estimation strategy'' evaluates the performance of a possible ANN from its design (without constructing and training it).
NAS is closely related to [[hyperparameter optimization]]<ref>Matthias Feurer and Frank Hutter. [https://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_1.pdf Hyperparameter optimization]. In: ''AutoML: Methods, Systems, Challenges'', pages 3–38.</ref> and [[meta-learning (computer science)|meta-learning]]<ref>{{Cite book|chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-05318-5_2|doi = 10.1007/978-3-030-05318-5_2|chapter = Meta-Learning|title = Automated Machine Learning|series = The Springer Series on Challenges in Machine Learning|year = 2019|last1 = Vanschoren|first1 = Joaquin|pages = 35–61|isbn = 978-3-030-05317-8|s2cid = 239362577}}</ref> and is a subfield of [[automated machine learning]] (AutoML).<ref>{{Cite journal |last=Salehin |first=Imrus |last2=Islam |first2=Md. Shamiul |last3=Saha |first3=Pritom |last4=Noman |first4=S. M. |last5=Tuni |first5=Azra |last6=Hasan |first6=Md. Mehedi |last7=Baten |first7=Md. Abu |date=2024-01-01 |title=AutoML: A systematic review on automated machine learning with neural architecture search |url=https://www.sciencedirect.com/science/article/pii/S2949715923000604 |journal=Journal of Information and Intelligence |volume=2 |issue=1 |pages=52–81 |doi=10.1016/j.jiixd.2023.10.002 |issn=2949-7159}}</ref>
==Reinforcement learning==
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