Results 31 to 40 of about 42,332 (292)
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
CoRR, 2022 The 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.
Han Wang 0066 +9 more
openaire +3 more sources
Xgboost hyperparameter tuning result.
, 2022 Xgboost hyperparameter tuning result.
Antonio Sanfilippo (127041) +8 more
core +1 more source
Rethinking the Hyperparameters for Fine-tuning
CoRR, 2020 Published as a conference paper at ICLR ...
Hao Li +6 more
openaire +3 more sources
Hyperparameters and tuning strategies for random forest [PDF]
WIREs Data Mining and Knowledge Discovery, 2019 The random forest (RF) algorithm has several hyperparameters that have to be set by the user, for example, the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain, and ...
Philipp Probst +2 more
openaire +2 more sources
Hyperparameter tuning for SVR regressor.
, 2023 Hyperparameter tuning for SVR regressor.
Gabriel Cuevas (1475851) +5 more
core +1 more source
Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate
Frontiers in Public Health, 2021 This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction
Jayakumar Kaliappan +5 more
doaj +1 more source
Diagnostics, 2022 Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy.
Masyitah Abu +6 more
doaj +1 more source
Hyperparameter tuning of machine learning algorithms.
, 2022 Hyperparameter tuning of machine learning algorithms.
Yutao Xue (12451975) +4 more
core +1 more source
Hyperparameter tuning result by fold: MLP.
, 2021 Hyperparameter tuning result by fold: MLP.
Sang Ok Choi (11426728) +2 more
core +1 more source