Gaussian process regression for multivariate spectroscopic calibration

By Tao Chen, Julian Morris, Elaine B. Martin. 2007

166 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/3033885b60c8ea3f219b236b70152b6e27f3d68c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Bo Wang
2. Denis Ribeiro do Valle
3. Bani K. Mallick
4. Tao Chen
5. Xuesong Zhang
6. Kaiguang Zhao
7. Sorin C. Popescu

In Biophysics

1. Bo Wang
2. Tao Chen

In Control and Systems Engineering

1. Bo Wang
2. Bani K. Mallick
3. Tao Chen
4. Xuesong Zhang

Citations in the corpus

• Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection (2013, background): “…is concomitantly complemented by advances in statistical learning that offer an expanding suite of tools to explore empirical relationships between spectra and canopy biochemical/biophysical properties (e.g., Gaussian processes regression as used in Chen et al., 2007; Zhao et al., 2008).”
• Gaussian process regression with multiple response variables (2015, methodology): “In chemometrics and related areas, GPR has been applied to calibration of spectroscopic analysers [6, 16, 26], response surface modelling [27], dynamic process modelling [11, 15], system identification [5] and ensemble learning [12, 26], among others.”