Publications (41)
Fast Polynomial Kernel Classification for Massive Data
Jinshan Zeng, Minrun Wu, Shao-Bo Lin +1
In the era of big data, it is desired to develop efficient machine learning algorithms to tackle massive data challenges such as storage bottleneck, algorithmic scalability, and in…
Adaptive Parameter Selection for Kernel Ridge Regression
Shao-Bo Lin
This paper focuses on parameter selection issues of kernel ridge regression (KRR). Due to special spectral properties of KRR, we find that delicate subdivision of the parameter int…
Distributed Uncertainty Quantification of Kernel Interpolation on Spheres
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 i…
Integral Operator Approaches for Scattered Data Fitting on Spheres
Shao-Bo Lin
This paper focuses on scattered data fitting problems on spheres. We study the approximation performance of a class of weighted spectral filter algorithms, including Tikhonov regul…
Kernel-based L_2-Boosting with Structure Constraints
Yao Wang, Xin Guo, Shao-Bo Lin
Developing efficient kernel methods for regression is very popular in the past decade. In this paper, utilizing boosting on kernel-based weaker learners, we propose a novel kernel-…
Realization of spatial sparseness by deep ReLU nets with massive data
Charles K. Chui, Shao-Bo Lin, Bo Zhang +1
The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognize…