Results 61 to 70 of about 100,134 (161)
UTILIZING GAUSSIAN PROCESS REGRESSION FOR NONLINEAR MAGNETIC SEPARATION PROCESS IDENTIFICATION
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie ŚrodowiskaThis paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes.
Oleksandr Volovetskyi
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Improving soil moisture prediction using Gaussian process regression
Smart Agricultural TechnologySoil moisture plays a vital role in agriculture and hydrology, influencing key processes like plant growth and evaporation. Recent advancements in soil moisture monitoring have improved our ability to measure it at different scales, but challenges ...
Xiaomo Zhang, Xin Sun, Zhulu Lin
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Reinforcement learning with Gaussian process regression using variational free energy
Journal of Intelligent Systems, 2023 The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm.
Kameda Kiseki, Tanaka Fuyuhiko
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Gaussian process regression‐based load forecasting model
IET Generation, Transmission & DistributionIn this paper, Gaussian Process Regression (GPR)‐based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed.
Anamika Yadav +4 more
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Gaussian Process Regression for Swaption Cube Construction under No-Arbitrage Constraints
Risks, 2022 In this paper, we introduce a 3D finite dimensional Gaussian process (GP) regression approach for learning arbitrage-free swaption cubes. Based on the possibly noisy observations of swaption prices, the proposed ‘constrained’ GP regression approach is ...
Areski Cousin +2 more
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Gaussian Process Regression with Local Explanation
CoRR, 2020 Gaussian process regression (GPR) is a fundamental model used in machine learning. Owing to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications.
Yuya Yoshikawa, Tomoharu Iwata
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Locally Smoothed Gaussian Process Regression
Procedia Computer Science, 2022 We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation.
Davit Gogolashvili +2 more
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Ain Shams Engineering JournalThis study investigated the use of advanced machine learning techniques to model disintegration time for solid dosage oral formulations. The input features encompass molecular properties, physical attributes, excipient compositions, and formulation ...
Mohammed Ghazwani, Umme Hani
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Constructing coarse-grained models with physics-guided Gaussian process regression
APL Machine LearningCoarse-grained models describe the macroscopic mean response of a process at large scales, which derives from stochastic processes at small scales. Common examples include accounting for velocity fluctuations in a turbulent fluid flow model and cloud ...
Yating Fang +3 more
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Deriving Operating Rules of Hydropower Reservoirs Using Gaussian Process Regression
IEEE Access, 2019 Operating rules have been widely used to decide reservoir operations because they can help operators make an approximately optimal decision with limited runoff forecast information.
Benjun Jia +4 more
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