Results 21 to 30 of about 131,851 (273)
RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm
Mathematics, 2021 This work proposes a new algorithm for optimizing hyper-parameters of a machine learning algorithm, RHOASo, based on conditional optimization of concave asymptotic functions.
Ángel Luis Muñoz Castañeda +2 more
doaj +1 more source
Optimization of hyperparameters for SMS reconstruction [PDF]
Magnetic Resonance Imaging, 2020 Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices simultaneously. Overlapping slices are then separated using a mathematical model. Several parameters used in SMS reconstruction impact the quality of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure
Muftuler, L. Tugan +7 more
openaire +3 more sources
Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting
Energies, 2020 Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies.
Gabriel Trierweiler Ribeiro +4 more
doaj +1 more source
IEEE Access, 2021 As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency.
Zhen Li, Yang Wang, Jianeng Ma
doaj +1 more source
Bayesian off-line detection of multiple change-points corrupted by multiplicative noise : application to SAR image edge detection [PDF]
, 2003 This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied.
Andre-Obrecht +39 more
core +3 more sources
Theoretical Aspects in Penalty Hyperparameters Optimization
Mediterranean Journal of Mathematics, 2023 AbstractLearning processes play an important role in enhancing understanding and analyzing real phenomena. Most of these methodologies revolve around solving penalized optimization problems. A significant challenge arises in the choice of the penalty hyperparameter, which is typically user-specified or determined through Grid search approaches.
Esposito F., Selicato L., Sportelli C.
openaire +4 more sources
Symbolic Explanations for Hyperparameter Optimization
International Conference on AutoML, 2023 Hyperparameter optimization (HPO) methods can determine well-performing hyperparameter configurations efficiently but often lack insights and transparency. We propose to apply symbolic regression to meta-data collected with Bayesian optimization (BO) during HPO.
Segel, Sarah +4 more
openaire +3 more sources
Frugal Optimization for Cost-related Hyperparameters
, 2020 The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost.
Huang, Silu, Wang, Chi, Wu, Qingyun
core +2 more sources
IEEE Access, 2023 Deep Reinforcement Learning (DRL) allows agents to make decisions in a specific environment based on a reward function, without prior knowledge. Adapting hyperparameters significantly impacts the learning process and time.
Ebrahim Hamid Hasan Sumiea +6 more
doaj +1 more source
Promoting Fairness through Hyperparameter Optimization [PDF]
2021 IEEE International Conference on Data Mining (ICDM), 2021 Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric- or model-specific, require access to sensitive attributes at inference time, or carry high development or deployment costs.
Cruz, André F. +4 more
openaire +2 more sources