Results 261 to 270 of about 42,332 (292)
Some of the next articles are maybe not open access.
Sequential Model-Free Hyperparameter Tuning
2015 IEEE International Conference on Data Mining, 2015Hyperparameter tuning is often done manually but current research has proven that automatic tuning yields effective hyperparameter configurations even faster and does not require any expertise. To further improve the search, recent publications propose transferring knowledge from previous experiments to new experiments.
Martin Wistuba +2 more
openaire +1 more source
An Approach to Tuning Hyperparameters in Parallel: An Approach to Tuning Hyperparameters in Parallel
2019Predicting violent storms and dangerous weather conditions, for instance predicting tornados, is an important application for public safety. Using numerical weather simulations to classify a weather pattern as tornadic or not tornadic can take a long time due to the immense complexity associated with current models.
openaire +1 more source
Tuning SVM hyperparameters in the primal
2010 Second International Conference on Computational Intelligence and Natural Computing, 2010Choosing optimal hyperparameters for Support Vector Machines(SVMs) is quite difficult but extremely essential in SVM design. This is usually done by minimizing estimates of generalization error such as the k-fold cross-validation error or the upper bound of leave-one-out(LOO) error.
null Huang Dongyuan, null Chen Xiaoyun
openaire +1 more source
Kriging Hyperparameter Tuning Strategies
AIAA Journal, 2008Response surfaces have been extensively used as a method of building effective surrogate models of high-fidelity computational simulations. Of the numerous types of response surface models, kriging is perhaps one of the most effective, due to its ability to model complicated responses through interpolation or regression of known data while providing an
Toal, David J.J. +2 more
openaire +2 more sources
No More Pesky Hyperparameters: Offline Hyperparameter Tuning For Reinforcement Learning
2021The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous, or time consuming.
openaire +1 more source
Hyperparameter Tuning in Offline Reinforcement Learning
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022Andrew Tittaferrante, Abdulsalam Yassine
openaire +1 more source
Grid search hyperparameter tuning in additive manufacturing processes
Manufacturing Letters, 2023Salil Desai
exaly
Population-Based Hyperparameter Tuning With Multitask Collaboration
IEEE Transactions on Neural Networks and Learning Systems, 2023Wing W Y Ng, Wendi Li, Ting Wang
exaly