Rectangularization of Gaussian process regression for optimization of hyperparameters
Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR uses a square matrix of covariances among training data and can be viewed as linear regression problem with equal numbers...
Main Authors: | Sergei Manzhos, Manabu Ihara |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-09-01
|
Series: | Machine Learning with Applications |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827023000403 |
Similar Items
-
Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System
by: Yanis Hamoudi, et al.
Published: (2023-06-01) -
Gaussian Process Regression´s Hyperparameters Optimization to Predict Financial Distress
by: Jakub Horak, et al.
Published: (2023-09-01) -
Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb
by: Jia Wu, et al.
Published: (2019-03-01) -
Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms
by: Elahe Akbari, et al.
Published: (2023-07-01) -
Enhanced Fault Localization in Multi-Terminal HVDC Systems Using Improved Gaussian Process Regression
by: Abha Pragati, et al.
Published: (2024-01-01)