论文信息 - Use of Gaussian Process Regression in Processing Scientific Data

Use of Gaussian Process Regression in Processing Scientific Data

We present the method of Gaussian Process Regression (GPR) and discuss the issues of training and prediction and their ecient realization. Further, we consider the application of GPR in design optimization and modeling experimental data.

J. Hajek

[1] Carl E. Rasmussen,et al. A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[2] R. K. Ursem. Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[3] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .

[4] Neil D. Lawrence,et al. Fast Forward Selection to Speed Up Sparse Gaussian Process Regression , 2003, AISTATS.

[5] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[6] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..