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Proc. SciPy 2013.</ref><ref>Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown: [https://arxiv.org/abs/1208.3719 Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms]. KDD 2013: 847–855</ref><ref>Jasper Snoek, Hugo Larochelle and Ryan Prescott Adams. [https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf Practical Bayesian Optimization of Machine Learning Algorithms]. Advances in Neural Information Processing Systems, 2012</ref> [[reinforcement learning]],<ref>{{Cite thesis |title=Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics |url=https://www.research-collection.ethz.ch/handle/20.500.11850/370833 |publisher=ETH Zurich |date=2019 |degree=Doctoral Thesis |doi=10.3929/ethz-b-000370833 |language=en |first=Felix |last=Berkenkamp|hdl=20.500.11850/370833 }}</ref> planning, visual attention, architecture configuration in [[deep learning]], static program analysis, experimental [[particle physics]],<ref>Philip Ilten, Mike Williams, Yunjie Yang. [https://arxiv.org/abs/1610.08328 Event generator tuning using Bayesian optimization]. 2017 JINST 12 P04028. DOI: 10.1088/1748-0221/12/04/P04028</ref><ref>Evaristo Cisbani et al. [https://iopscience.iop.org/article/10.1088/1748-0221/15/05/P05009 AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case] 2020 JINST 15 P05009. DOI: 10.1088/1748-0221/15/05/P05009</ref> quality-diversity optimization,<ref>{{Cite arXiv |last1=Kent |first1=Paul |last2=Gaier |first2=Adam |last3=Mouret |first3=Jean-Baptiste |last4=Branke |first4=Juergen |date=2023-07-19 |title=BOP-Elites, a Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions |class=math.OC |eprint=2307.09326}} Preprint: Arxiv.</ref><ref>{{Cite book |last1=Kent |first1=Paul |last2=Branke |first2=Juergen |title=Proceedings of the Genetic and Evolutionary Computation Conference |chapter=Bayesian Quality Diversity Search with Interactive Illumination |date=2023-07-12 |chapter-url=https://dl.acm.org/doi/10.1145/3583131.3590486 |series=GECCO '23 |___location=New York, NY, USA |publisher=Association for Computing Machinery |pages=1019–1026 |doi=10.1145/3583131.3590486 |isbn=979-8-4007-0119-1|s2cid=259833672 }}</ref><ref>{{Cite journal |last1=Gaier |first1=Adam |last2=Asteroth |first2=Alexander |last3=Mouret |first3=Jean-Baptiste |date=2018-09-01 |title=Data-Efficient Design Exploration through Surrogate-Assisted Illumination |url=http://dx.doi.org/10.1162/evco_a_00231 |journal=Evolutionary Computation |volume=26 |issue=3 |pages=381–410 |doi=10.1162/evco_a_00231 |pmid=29883202 |s2cid=47003986 |issn=1063-6560|doi-access=free |arxiv=1806.05865 }}</ref> chemistry, material design, and drug development.<ref name=":0" /><ref>Gomez-Bombarelli et al. [https://pubs.acs.org/doi/10.1021/acscentsci.7b00572 Automatic Chemical Design using a Data-Driven Continuous Representation of Molecules]. ACS Central Science, Volume 4, Issue 2, 268-276 (2018)</ref><ref>Griffiths et al. [https://pubs.rsc.org/en/content/articlehtml/2020/sc/c9sc04026a Constrained Bayesian Optimization for Automatic Chemical Design using Variational Autoencoders] Chemical Science: 11, 577-586 (2020)</ref>
In facial recognition<ref name=“Bouchene2023”>Mohammed Mehdi Bouchene: [Bayesian Optimization of Histogram of Oriented Gradients (Hog) Parameters for Facial Recognition. SSRN (2023)</ref>. In the field of facial recognition, the performance of the Histogram of Oriented Gradients (HOG) algorithm, a popular feature extraction method, heavily relies on its parameter settings. Optimizing these parameters can be challenging but crucial for achieving high accuracy<ref name=“Bouchene2023”/>. A novel approach to optimize the HOG algorithm parameters and image size for facial recognition using a Tree-structured Parzen Estimator (TPE) based Bayesian optimization technique has been proposed<ref name=“Bouchene2023”/>. This optimized approach has the potential to be adapted for other computer vision applications and contributes to the ongoing development of hand-crafted parameter-based feature extraction algorithms in computer vision<ref name=“Bouchene2023”/>.
==See also==
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