Bayesian optimization: Difference between revisions

Revision as of 13:10, 20 September 2024

Revision as of 13:11, 20 September 2024

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| isbn=978-1107163447

| url = https://www.probabilistic-numerics.org/assets/ProbabilisticNumerics.pdf

}}</ref> that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions. With the rise of [[artificial intelligence]] innovation in the 21st century, Bayesian optimizations have found prominent use in [[machine learning]] problems, for optimisingoptimizing hyperparameter values.<ref>{{cite journal |first=Jasper |last=Snoek |title=Practical Bayesian Optimization of Machine Learning Algorithms |journal=Advances in Neural Information Processing Systems 25 (NIPS 2012) |year=2012}}</ref><ref>{{cite journal |first=Aaron |last=Klein |title=Fast bayesian optimization of machine learning hyperparameters on large datasets |journal=Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR |year=2017 |pages=528-536}}</ref>

==History==