Results 81 to 90 of about 100,134 (161)
Multivariate Interpolation of Wind Field Based on Gaussian Process Regression
Atmosphere, 2018 The resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm.
Miao Feng +5 more
doaj +1 more source
Gaussian Process Regression with Mismatched Models [PDF]
, 2002 7 pages, style file nips01e.sty ...
openaire +3 more sources
Proper Complex Gaussian Processes for Regression
CoRR, 2015 Complex-valued signals are used in the modeling of many systems in engineering and science, hence being of fundamental interest. Often, random complex-valued signals are considered to be proper. A proper complex random variable or process is uncorrelated with its complex conjugate.
Rafael Boloix-Tortosa +3 more
openaire +2 more sources
Numerical Solutions of Hamilton-Jacobi Inequalities by Constrained Gaussian Process Regression
SICE Journal of Control, Measurement, and System Integration, 2018 This paper proposes numerical solutions of Hamilton-Jacobi inequalities based on constrained Gaussian process regression. While Gaussian process regression is a tool to estimate an unknown function from its input and output data conventionally, the ...
Kenji Fujimoto +2 more
doaj +1 more source
Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression
International Journal of Aerospace Engineering, 2019 In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors.
Haopeng Zhang +4 more
doaj +1 more source
3D Radiation Mapping Using Gaussian Process Regression with Intensity Projection
Advanced Intelligent SystemsThis article presents a novel approach for generating a three‐dimensional radiation map using data collected by mobile robots, aimed at monitoring radiation distribution in environments such as nuclear power plants.
Jihoon Jung +3 more
doaj +1 more source
Deck motion prediction using neural kernel network Gaussian process regression
Xibei Gongye Daxue XuebaoDeck motion prediction and compensation are critical technologies for carrier-based aircraft automatic landing. Traditional deck motion prediction methods rely on precision of motion models and parameter adjustments, facing challenges in adaptability to ...
QIN Peng +3 more
doaj +1 more source
Differentially Private Regression with Gaussian Processes.
, 2018 A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the differential privacy framework with the flexibility of Gaussian processes (GPs). We propose a method using GPs to provide differentially private (DP) regression.
Smith, M.T. +3 more
openaire +4 more sources
Teaching Functions with Gaussian Process Regression
Proceedings of the AAAI Symposium SeriesHumans are remarkably adaptive instructors who adjust advice based on their estimations about a learner’s prior knowledge and current goals. Many topics that people teach, like goal-directed behaviors, causal systems, categorization, and time-series patterns, have an underlying commonality: they map inputs to outputs through an unknown function.
Maya Malaviya, Mark K. Ho
openaire +2 more sources
A Gaussian process guide for signal regression in magnetic fusion
Nuclear FusionExtracting reliable information from diagnostic data in tokamaks is critical for understanding, analyzing, and controlling the behavior of fusion plasmas and validating models describing that behavior.
Craig Michoski +8 more
doaj +1 more source