Results 71 to 80 of about 100,134 (161)
The pitfalls of using Gaussian Process Regression for normative modeling.
PLoS ONE, 2021 Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity.
Bohan Xu +3 more
doaj +1 more source
Privacy-Aware Gaussian Process Regression
TechnometricsWe propose a novel theoretical and methodological framework for Gaussian process regression subject to privacy constraints. The proposed method can be used when a data owner is unwilling to share a high-fidelity supervised learning model built from their data with the public due to privacy concerns.
Tuo, Rui +2 more
openaire +2 more sources
Optimal querying for communication-efficient ADMM using Gaussian process regression
Franklin OpenIn distributed optimization schemes consisting of a group of agents connected to a central coordinator, the optimization algorithm often involves the agents solving private local sub-problems and exchanging data frequently with the coordinator to solve ...
Aldo Duarte +2 more
doaj +1 more source
Benchmarking of quantum fidelity kernels for Gaussian process regression
Machine Learning: Science and TechnologyQuantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest.
Xuyang Guo, Jun Dai, Roman V Krems
doaj +1 more source
Tensor Regression Meets Gaussian Processes
CoRR, 2017 17 ...
Rose Yu, Max Guangyu Li, Yan Liu 0002
openaire +3 more sources
Link quality prediction model based on Gaussian process regression
Tongxin xuebao, 2018 Link quality is an important factor of reliable communication and the foundation of upper protocol design for wireless sensor network.Based on this,a link quality prediction model based on Gaussian process regression was proposed.It employed grey ...
Jian SHU +4 more
doaj +2 more sources
Efficient Electromagnetic Near-Field Scanning Using Physics-Informed Gaussian Process Regression
IEEE AccessThis paper proposes a novel approach combining prior physics-based Gaussian Process Regression (GPR) with Bayesian Optimization for efficient and accurate electromagnetic near-field scanning.
Tomas Monopoli +4 more
doaj +1 more source
Gaussian Process Regression with Measurement Error
IEICE Transactions on Information and Systems, 2010 Regression analysis that incorporates measurement errors in input variables is important in various applications. In this study, we consider this problem within a framework of Gaussian process regression. The proposed method can also be regarded as a generalization of kernel regression to include errors in regressors.
Yukito Iba, Shotaro Akaho
openaire +2 more sources
Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression
Journal of Applied Mathematics, 2014 This paper solves the dynamic traveling salesman problem (DTSP) using dynamic Gaussian Process Regression (DGPR) method. The problem of varying correlation tour is alleviated by the nonstationary covariance function interleaved with DGPR to generate a ...
Stephen M. Akandwanaho +2 more
doaj +1 more source
Curb Detection and Mapping via Robust Iterative Gaussian Process Regression
Journal of Highway and Transportation Research and DevelopmentCurb detection and mapping are of great importance to ensure the safety and efficiency of intelligent vehicles. However, it remains challenging because shape estimation under noise and outliers is not well addressed in real traffic scenarios.
Di Wang +4 more
doaj +1 more source