Distributed Uncertainty Quantification of Kernel Interpolation on Spheres

no code implementations • 25 Oct 2023 • Shao-Bo Lin, Xingping Sun, Di Wang

For radial basis function (RBF) kernel interpolation of scattered data, Schaback in 1995 proved that the attainable approximation error and the condition number of the underlying interpolation matrix cannot be made small simultaneously.

Uncertainty Quantification

Kernel Interpolation of High Dimensional Scattered Data

no code implementations • 3 Sep 2020 • Shao-Bo Lin, Xiangyu Chang, Xingping Sun

Data sites selected from modeling high-dimensional problems often appear scattered in non-paternalistic ways.

Clustering Vocal Bursts Intensity Prediction

Model selection of polynomial kernel regression

no code implementations • 7 Mar 2015 • Shaobo Lin, Xingping Sun, Zongben Xu, Jinshan Zeng

On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary.

Model Selection regression