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The Gaussian process directly models the sample distribution based on the kernel matrix, which can effectively describe the structural information between samples. The Gaussian process can learn generalization models, and thus is widely used in multi-task learning and transfer learning tasks.
Nov 19, 2023
May 20, 2024 · Gaussian processes for regression [10]. All results are relative to a baseline algorithm which is based entirely on the mean and median of the training.
mgpr: An R package for multivariate Gaussian process regression
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Oct 25, 2023 · Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation ...
Jun 3, 2024 · Gaussian Process Regression (GPR) is a powerful and flexible non-parametric regression technique used in machine learning and statistics.
Aug 26, 2024 · Gaussian processes are useful in statistical modelling, benefiting from properties inherited from the normal distribution. For example, if a random process is ...
Jul 15, 2024 · It combines Gaussian processes with reduced-order modeling to efficiently simulate the mechanics of solids while accounting for uncertainties. The reduced ...
Feb 16, 2024 · A Gaussian kernel offers smoothness, flexibility, and non-linearity in capturing complex relationships between data points.
Jan 15, 2024 · In addition, the Gaussian process regression method is used to build data-driven prediction models with different predictors that contribute significantly to ...
Feb 28, 2024 · The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data.
Feb 22, 2024 · This paper focuses on the estimation of the Gaussian process covariance parameters by reviewing recent works on the analysis of the advantages and disadvantages ...
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