Stechkin-Marchaud-Type Inequalities for Baskakov Polynomials. - Shunsheng Guo; Hongzhi Tong; Gengsheng Zhang (J. Approx. Theory, 2002)

Learning rates for regularized classifiers using multivariate polynomial kernels. - Hongzhi Tong; Di-Rong Chen; Lizhong Peng (J. Complex., 2008)

Analysis of Support Vector Machines Regression. - Hongzhi Tong; Di-Rong Chen; Lizhong Peng (Found. Comput. Math., 2009)

Least Square Regression with lp-Coefficient Regularization. - Hongzhi Tong; Di-Rong Chen; Fenghong Yang (Neural Comput., 2010)

Support vector machines regression with I1-regularizer. - Hongzhi Tong; Di-Rong Chen; Fenghong Yang (J. Approx. Theory, 2012)

Learning Rates for l1-Regularized Kernel Classifiers. - Hongzhi Tong; Di-Rong Chen; Fenghong Yang (J. Appl. Math., 2013)

Learning with Convex Loss and Indefinite Kernels. - Hongzhi Tong; Di-Rong Chen; Fenghong Yang (Neural Comput., 2014)

A Note on Support Vector Machines with Polynomial Kernels. - Hongzhi Tong (Neural Comput., 2016)

Learning performance of regularized moving least square regression. - Hongzhi Tong; Qiang Wu 003 (J. Comput. Appl. Math., 2017)

Analysis of regularized least squares for functional linear regression model. - Hongzhi Tong; Michael Ng 001 (J. Complex., 2018)

Calibration of ϵ-insensitive loss in support vector machines regression. - Hongzhi Tong; Michael K. Ng 001 (J. Frankl. Inst., 2019)

Analysis of Regression Algorithms with Unbounded Sampling. - Hongzhi Tong; Jiajing Gao (Neural Comput., 2020)

Convergence rates of support vector machines regression for functional data. - Hongzhi Tong (J. Complex., 2022)

Functional linear regression with Huber loss. - Hongzhi Tong (J. Complex., 2023)

Nonasymptotic analysis of robust regression with modified Huber's loss. - Hongzhi Tong (J. Complex., 2023)

Spectral algorithms for learning with dependent observations. - Hongzhi Tong; Michael Ng 001 (J. Comput. Appl. Math., 2024)

Least squares regression under weak moment conditions. - Hongzhi Tong (J. Comput. Appl. Math., 2025)