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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
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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
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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
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Massively parallel approximate Gaussian process regression [PDF]
, 2014 We explore how the big-three computing paradigms -- symmetric multi-processor (SMC), graphical processing units (GPUs), and cluster computing -- can together be brought to bare on large-data Gaussian processes (GP) regression problems via a careful ...
Gramacy, Robert +3 more
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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
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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
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Variational Bayesian multinomial probit regression with Gaussian process priors [PDF]
, 2005 It is well known in the statistics literature that augmenting binary and polychotomous response models with Gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior.
Girolami, M., Rogers, S.
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Gaussian Process Regression Networks [PDF]
, 2011 17 pages, 3 figures, 1 table.
Andrew Gordon Wilson +2 more
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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
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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
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