Results 31 to 40 of about 100,134 (161)
Hierarchical Facial Age Estimation Using Gaussian Process Regression
IEEE Access, 2019 Automatic age estimation from facial images has attracted increasing attention due to its promising potential in real-life computer vision applications. However, due to uncontrollable environments, insufficient and incomplete training data, strong person-
Manisha M. Sawant, Kishor Bhurchandi
doaj +1 more source
Rectangularization of Gaussian process regression for optimization of hyperparameters
Machine Learning with Applications, 2023 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 ...
Sergei Manzhos, Manabu Ihara
doaj +1 more source
Barrier distribution extraction via Gaussian process regression [PDF]
EPJ Web of ConferencesThis work presents a novel method for extracting potential barrier distributions from experimental fusion cross sections. We utilize a simple Gaussian process regression (GPR) framework to model the observed cross sections as a function of energy for ...
Godbey Kyle
doaj +1 more source
mgpr: An R package for multivariate Gaussian process regression
SoftwareX, 2023 Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables.
Petri Varvia +2 more
doaj +1 more source
Gaussian Process Regression Networks [PDF]
, 2011 17 pages, 3 figures, 1 table.
Andrew Gordon Wilson +2 more
openaire +3 more sources
Projection pursuit Gaussian process regression
IISE Transactions, 2022 A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer from the curse of dimensionality, when the input dimension is relatively high given limited data points. Gaussian process models with additive correlation functions are scalable
Gecheng Chen, Rui Tuo
openaire +2 more sources
Energy-Driven Image Interpolation Using Gaussian Process Regression
Journal of Applied Mathematics, 2012 Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing.
Lingling Zi, Junping Du
doaj +1 more source
Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller
IEEE Access, 2023 This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the
Hyuntae Kim, Hamin Chang
doaj +1 more source
Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression
Applied Sciences, 2022 During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-
Mahmood Ahmad +6 more
doaj +1 more source
Transport Gaussian Processes for Regression
CoRR, 2020 Gaussian process (GP) priors are non-parametric generative models with appealing modelling properties for Bayesian inference: they can model non-linear relationships through noisy observations, have closed-form expressions for training and inference, and are governed by interpretable hyperparameters.
openaire +2 more sources