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Sparse multiscale gaussian process regression [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with ...
Christian Walder +2 more
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Online semi-supervised multi-person tracking with gaussian process regression [PDF]
MATEC Web of Conferences, 2019 Most existing multi-person tracking approaches are affected by lighting condition, pedestrian pose change abruptly, scale changes, realtime processing to name a few, resulting in detection error, drift and other issues.
Zhang Baobing +4 more
doaj +1 more source
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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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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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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Gaussian process model based predictive control [PDF]
, 2003 Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack ...
Kocijan, J. +17 more
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Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting
Frontiers in Earth Science, 2022 Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide hazards.
Weiqi Yang +3 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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Gaussian Process Regression Networks [PDF]
, 2011 17 pages, 3 figures, 1 table.
Andrew Gordon Wilson +2 more
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Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process [PDF]
, 2012 16/01/14 meb. pre-print version OK to add. statement added.In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing ...
Soh, Harold +3 more
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