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m →Gradient-based optimization: task, replaced: Advances in Neural Information Processing Systems 2 → Advances in Neural Information Processing Systems |volume=2 |
Adding local short description: "Machine learning problem", overriding Wikidata description "choosing a set of optimal hyperparameters for a learning algorithm" |
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{{Short description|Machine learning problem}}
In [[machine learning]], '''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> or tuning is the problem of choosing a set of optimal [[Hyperparameter (machine learning)|hyperparameters]] for a learning algorithm. A hyperparameter is a [[parameter]] whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.
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