Results 121 to 130 of about 100,134 (161)

Incorporating Noncovalent Interactions in Transfer Learning Gaussian Process Regression Models for Molecular Simulations. [PDF]

J Chem Theory Comput
Brown ML, Isamura BK, Skelton JM, Popelier PLA.   +3 more
europepmc   +1 more source

Enhanced prediction of bolt support drilling pressure using optimized Gaussian process regression. [PDF]

Sci Rep
Liu J.
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Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach. [PDF]

Mil Med
Upadhyay K, Jagani R, Giovanis DG, Alshareef A, Knutsen AK, Johnson CL, Carass A, Bayly PV, Shields MD, Ramesh KT.   +9 more
europepmc   +1 more source

Effects of sensory combination on crispness and prediction of sensory evaluation value by Gaussian process regression. [PDF]

PLoS One
Nakamoto H, Nishimura R, Kobayashi F.
europepmc   +1 more source

Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs. [PDF]

Sci Rep
Hai T, Basem A, Alizadeh A, Sharma K, Jasim DJ, Rajab H, Ahmed M, Kassim M, Singh NSS, Maleki H.   +9 more
europepmc   +1 more source

Robust Gaussian Process Regression Method for Efficient Tunneling Pathway Optimization: Application to Surface Processes. [PDF]

J Chem Theory Comput
Fang W, Zhu YC, Cheng Y, Hao YP, Richardson JO.   +4 more
europepmc   +1 more source

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression. [PDF]

Sensors (Basel)
Chen J, Lin J, Zhang M, Lin Q.
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Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression. [PDF]

Sensors (Basel)
Van Brandt S, Kapusuz KY, Sennesael J, Lemey S, Van Torre P, Verhaevert J, Van Hecke T, Rogier H.   +7 more
europepmc   +1 more source

Predicting hydrogen atom transfer energy barriers using Gaussian process regression.

Digit Discov
Ulanov E, Qadir GA, Riedmiller K, Friederich P, Gräter F.   +4 more
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Bagging for Gaussian process regression

Neurocomputing, 2009
This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data are re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions.
Tao Chen 0009, Jianghong Ren
openaire   +3 more sources