Baohuai Sheng

20 papers C 1Journal 19
YearRankTypeTitle / Venue / Authors
2025 J jnl
Deterministic error analysis of kernel regularized regression for spherical scattered data.
Int. J. Wavelets Multiresolution Inf. Process.
Wan Li, Yeli Niu, Baohuai Sheng, Peixin Ye
2025 J jnl
On the convergence rate of two-stage sampling distribution regression.
Int. J. Wavelets Multiresolution Inf. Process.
Mingdang Tian, Chenmin Ni, Baohuai Sheng
2024 J jnl
On the density of translation networks defined on the unit ball.
Math. Found. Comput.
Xiaojun Sun, Baohuai Sheng, Lin Liu, Xiaoling Pan
2024 J jnl
Reproducing property of bounded linear operators and kernel regularized least square regressions.
Int. J. Wavelets Multiresolution Inf. Process.
Baohuai Sheng
2023 J jnl
Error analysis of kernel regularized pairwise learning with a strongly convex loss.
Math. Found. Comput.
Shuhua Wang, Baohuai Sheng
2022 J jnl
Some Upper Bounds for RKHS Approximation by Bessel Functions.
Axioms
Mingdang Tian, Baohuai Sheng, Shuhua Wang
2022 J jnl
The kernel regularized learning algorithm for solving Laplace equation with Dirichlet boundary.
Int. J. Wavelets Multiresolution Inf. Process.
Baohuai Sheng, Dapao Zhou, Shuhua Wang
2021 J jnl
Error analysis of the kernel regularized regression based on refined convex losses and RKBSs.
Int. J. Wavelets Multiresolution Inf. Process.
Baohuai Sheng, Lan Zuo
2019 J jnl
Convergence rate of SVM for kernel-based robust regression.
Int. J. Wavelets Multiresolution Inf. Process.
Shuhua Wang, Zhenlong Chen, Baohuai Sheng
2018 J jnl
The convergence rate of semi-supervised regression with quadratic loss.
Appl. Math. Comput.
Baohuai Sheng, Hancan Zhu
2017 J jnl
The improved learning rate for regularized regression with RKBSs.
Int. J. Mach. Learn. Cybern.
Huanxiang Liu, Baohuai Sheng, Peixin Ye
2017 J jnl
The performance of semi-supervised Laplacian regularized regression with the least square loss.
Int. J. Wavelets Multiresolution Inf. Process.
Baohuai Sheng, Daohong Xiang
2015 J jnl
Convergence rate of semi-supervised gradient learning algorithms.
Int. J. Wavelets Multiresolution Inf. Process.
Baohuai Sheng, Daohong Xiang, Peixin Ye
2014 J jnl
Strong Convex Loss Can Increase the Learning Rates of Online Learning.
J. Comput.
Baohuai Sheng, Liqin Duan, Pei-Xin Ye
2012 J jnl
Learning Rates of Support Vector Machine Classifiers with Data Dependent Hypothesis Spaces.
J. Comput.
Baohuai Sheng, Pei-Xin Ye
2012 J jnl
Optimal order of truncation and aliasing errors for multi-dimensional Whittaker-Shannon sampling expansion.
Int. J. Wirel. Mob. Comput.
Pei-Xin Ye, Baohuai Sheng, Xiuhua Yuan
2011 J jnl
Least Square Regression Learning with Data Dependent Hypothesis and Coefficient Regularzation.
J. Comput.
Baohuai Sheng, Pei-Xin Ye
2009 C conf
On Integrity of Harary Graphs.
COCOA
Fengwei Li, Qingfang Ye, Baohuai Sheng
2008 J jnl
The covering number for some Mercer kernel Hilbert spaces.
J. Complex.
Baohuai Sheng, Jianli Wang, Ping Li
2007 J jnl
On Approximation By Reproducing Kernel Spaces in Weighted L
J. Syst. Sci. Complex.
Baohuai Sheng