Hongzhi Tong

18 papers Journal 18
YearRankTypeTitle / Venue / Authors
2026 J jnl
Statistical analysis of prediction in functional polynomial quantile regression.
J. Complex.
Hongzhi Tong
2025 J jnl
Least squares regression under weak moment conditions.
J. Comput. Appl. Math.
Hongzhi Tong
2024 J jnl
Spectral algorithms for learning with dependent observations.
J. Comput. Appl. Math.
Hongzhi Tong, Michael Ng
2023 J jnl
Functional linear regression with Huber loss.
J. Complex.
Hongzhi Tong
2023 J jnl
Nonasymptotic analysis of robust regression with modified Huber's loss.
J. Complex.
Hongzhi Tong
2022 J jnl
Convergence rates of support vector machines regression for functional data.
J. Complex.
Hongzhi Tong
2020 J jnl
Analysis of Regression Algorithms with Unbounded Sampling.
Neural Comput.
Hongzhi Tong, Jiajing Gao
2019 J jnl
Calibration of ϵ-insensitive loss in support vector machines regression.
J. Frankl. Inst.
Hongzhi Tong, Michael K. Ng
2018 J jnl
Analysis of regularized least squares for functional linear regression model.
J. Complex.
Hongzhi Tong, Michael Ng
2017 J jnl
Learning performance of regularized moving least square regression.
J. Comput. Appl. Math.
Hongzhi Tong, Qiang Wu
2016 J jnl
A Note on Support Vector Machines with Polynomial Kernels.
Neural Comput.
Hongzhi Tong
2014 J jnl
Learning with Convex Loss and Indefinite Kernels.
Neural Comput.
Hongzhi Tong, Di-Rong Chen, Fenghong Yang
2013 J jnl
Learning Rates for l
J. Appl. Math.
Hongzhi Tong, Di-Rong Chen, Fenghong Yang
2012 J jnl
Support vector machines regression with I
J. Approx. Theory
Hongzhi Tong, Di-Rong Chen, Fenghong Yang
2010 J jnl
Least Square Regression with
Neural Comput.
Hongzhi Tong, Di-Rong Chen, Fenghong Yang
2009 J jnl
Analysis of Support Vector Machines Regression.
Found. Comput. Math.
Hongzhi Tong, Di-Rong Chen, Lizhong Peng
2008 J jnl
Learning rates for regularized classifiers using multivariate polynomial kernels.
J. Complex.
Hongzhi Tong, Di-Rong Chen, Lizhong Peng
2002 J jnl
Stechkin-Marchaud-Type Inequalities for Baskakov Polynomials.
J. Approx. Theory
Shunsheng Guo, Hongzhi Tong, Gengsheng Zhang