Compositional kernel learning using tree-based genetic programming for Gaussian process regression
- 1 September 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Structural and Multidisciplinary Optimization
- Vol. 62 (3), 1313-1351
- https://doi.org/10.1007/s00158-020-02559-7
Abstract
No abstract availableKeywords
Funding Information
- Korea Institute of Civil Engineering and Building Technology (Strategic Research Project (Smart Monitoring System for Concrete Structures Using FRP Nerve Sensor))
This publication has 41 references indexed in Scilit:
- Two-Stage Sensitivity-Based Group Screening in Computer ExperimentsTechnometrics, 2012
- Gaussian process emulators for the stochastic finite element methodInternational Journal for Numerical Methods in Engineering, 2011
- A review and comparison of four commonly used Bayesian and maximum likelihood model selection toolsEcological Modelling, 2007
- Modeling Data from Computer Experiments: An Empirical Comparison of Kriging with MARS and Projection Pursuit RegressionQuality Engineering, 2007
- Genetic Programming for the Identification of Nonlinear Input−Output ModelsIndustrial & Engineering Chemistry Research, 2005
- Application-driven sequential designs for simulation experiments: Kriging metamodellingJournal of the Operational Research Society, 2004
- Interpolation of Spatial Data: Some Theory for KrigingJournal of the American Statistical Association, 2000
- Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface PredictionTechnometrics, 1993
- Screening, Predicting, and Computer ExperimentsTechnometrics, 1992
- Multivariate Adaptive Regression SplinesThe Annals of Statistics, 1991