Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Frederik Rehbach"'
Autor:
Frederik Rehbach, Martin Zaefferer, Andreas Fischbach, Gunter Rudolph, Thomas Bartz-Beielstein
Publikováno v:
IEEE Transactions on Evolutionary Computation. 26:1365-1379
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b3532d744fb02c16306cb131f1aba1f
https://doi.org/10.1109/tevc.2022.3163843
https://doi.org/10.1109/tevc.2022.3163843
Publikováno v:
Hyperparameter Tuning for Machine and Deep Learning with R ISBN: 9789811951695
This case study gives a hands-on description of Hyperparameter Tuning (HPT) methods discussed in this book. The Random Forest (RF) method and its implementation was chosen because it is the method of the first choice in many Machine Learning (ML) tas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9165aaa015a7cbd612b75022bd4cfcf
https://doi.org/10.1007/978-981-19-5170-1_8
https://doi.org/10.1007/978-981-19-5170-1_8
Publikováno v:
Hyperparameter Tuning for Machine and Deep Learning with R ISBN: 9789811951695
This case study gives a hands-on description of Hyperparameter Tuning (HPT) methods discussed in this book. The Extreme Gradient Boosting (XGBoost) method and its implementation was chosen, because it is one of the most powerful methods in many Machi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32543f761d23573d12e5a8f0fbd9295c
https://doi.org/10.1007/978-981-19-5170-1_9
https://doi.org/10.1007/978-981-19-5170-1_9
Autor:
Frederik Rehbach
Publikováno v:
Enhancing Surrogate-Based Optimization Through Parallelization ISBN: 9783031306082
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d97b87b2259aca3d58a366e3de1007e4
https://doi.org/10.1007/978-3-031-30609-9_5
https://doi.org/10.1007/978-3-031-30609-9_5
Autor:
Frederik Rehbach
Publikováno v:
Enhancing Surrogate-Based Optimization Through Parallelization ISBN: 9783031306082
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8acd78d20f720bffafb0761b5a414c2a
https://doi.org/10.1007/978-3-031-30609-9_1
https://doi.org/10.1007/978-3-031-30609-9_1
Autor:
Frederik Rehbach
Publikováno v:
Enhancing Surrogate-Based Optimization Through Parallelization ISBN: 9783031306082
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::103fe4ed30688495f6e053a2f63f5969
https://doi.org/10.1007/978-3-031-30609-9_2
https://doi.org/10.1007/978-3-031-30609-9_2
Autor:
Frederik Rehbach
Publikováno v:
Studies in Computational Intelligence ISBN: 9783031306082
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b3de3b3f7596cf655175ebc7a3ee8e4
https://doi.org/10.1007/978-3-031-30609-9
https://doi.org/10.1007/978-3-031-30609-9
Autor:
Frederik Rehbach
Publikováno v:
Enhancing Surrogate-Based Optimization Through Parallelization ISBN: 9783031306082
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b11bad9797dbcf074576fc8a7f22a162
https://doi.org/10.1007/978-3-031-30609-9_3
https://doi.org/10.1007/978-3-031-30609-9_3
Publikováno v:
Hyperparameter Tuning for Machine and Deep Learning with R ISBN: 9789811951695
A surrogate model based Hyperparameter Tuning (HPT) approach for Deep Learning (DL) is presented. This chapter demonstrates how the architecture-level parameters (hyperparameters) of Deep Neural Networks (DNNs) that were implemented in / can be optim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5b79d7340b9571da6d3b609a2f537b7
https://doi.org/10.1007/978-981-19-5170-1_10
https://doi.org/10.1007/978-981-19-5170-1_10
Autor:
Frederik Rehbach
Publikováno v:
Enhancing Surrogate-Based Optimization Through Parallelization ISBN: 9783031306082
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c21fdc1faf1d930896379f69862a9336
https://doi.org/10.1007/978-3-031-30609-9_4
https://doi.org/10.1007/978-3-031-30609-9_4