Results 21 to 30 of about 374,385 (274)
Tunnel geomechanical parameters prediction using Gaussian process regression
Machine Learning with Applications, 2021 The purpose of this study is to apply a modern intelligent method of Gaussian process regression (GPR) to predict the geological parameter of Rock Quality Designation (RQD) along the tunnel route. This method can also be used for any geological parameter
Arsalan Mahmoodzadeh +6 more
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Gaussian process regression in the flat limit
The Annals of Statistics, 2023 Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regression. Most of the theoretical work on GP regression has focused on a large-$n$ asymptotics, i.e. as the amount of data increases.
Barthelme, Simon +3 more
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Heteroscedastic Gaussian process regression [PDF]
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Process regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori
Quoc V. Le +2 more
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Estimation of clustering parameters using gaussian process regression. [PDF]
PLoS ONE, 2014 We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information ...
Paul Rigby +2 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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Variational Tobit Gaussian Process Regression
Statistics and Computing, 2023 AbstractWe propose a variational inference-based framework for training a Gaussian process regression model subject to censored observational data. Data censoring is a typical problem encountered during the data gathering procedure and requires specialized techniques to perform inference since the resulting probabilistic models are typically ...
Marno Basson +2 more
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Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries [PDF]
, 2020 Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This paper derives machine learning-enabled calendar aging prediction for lithium-ion batteries.
Hu, Xiaosong +4 more
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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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Gaussian Process Regression on Nested Spaces
SIAM/ASA Journal on Uncertainty Quantification, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christophette Blanchet-Scalliet +3 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
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