Hongzhi Tong

Orcid: 0000-0001-7584-5719

According to our database1, Hongzhi Tong authored at least 16 papers between 2002 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Spectral algorithms for learning with dependent observations.
J. Comput. Appl. Math., February, 2024

2023
Nonasymptotic analysis of robust regression with modified Huber's loss.
J. Complex., June, 2023

Functional linear regression with Huber loss.
J. Complex., 2023

2022
Convergence rates of support vector machines regression for functional data.
J. Complex., 2022

2020
Analysis of Regression Algorithms with Unbounded Sampling.
Neural Comput., 2020

2019
Calibration of ϵ-insensitive loss in support vector machines regression.
J. Frankl. Inst., 2019

2018
Analysis of regularized least squares for functional linear regression model.
J. Complex., 2018

2017
Learning performance of regularized moving least square regression.
J. Comput. Appl. Math., 2017

2016
A Note on Support Vector Machines with Polynomial Kernels.
Neural Comput., 2016

2014
Learning with Convex Loss and Indefinite Kernels.
Neural Comput., 2014

2013
Learning Rates for l<sup>1</sup>-Regularized Kernel Classifiers.
J. Appl. Math., 2013

2012
Support vector machines regression with I<sup>1</sup>-regularizer.
J. Approx. Theory, 2012

2010
Least Square Regression with <i>l<sup>p</sup></i>-Coefficient Regularization.
Neural Comput., 2010

2009
Analysis of Support Vector Machines Regression.
Found. Comput. Math., 2009

2008
Learning rates for regularized classifiers using multivariate polynomial kernels.
J. Complex., 2008

2002
Stechkin-Marchaud-Type Inequalities for Baskakov Polynomials.
J. Approx. Theory, 2002


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