Jun Ma

According to our database, Jun Ma authored at least 27 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

A novel robust adaptive subspace learning framework for dimensionality reduction.

Weizhi Xiong, Guolin Yu, Jun Ma, Sheng Liu

Appl. Intell., October, 2024

Robust adaptive learning framework for semi-supervised pattern classification.

Jun Ma, Guolin Yu

Signal Process., 2024

Distribution-free Bayesian regularized learning framework for semi-supervised learning.

Jun Ma, Guolin Yu

Neural Networks, 2024

Sparse robust adaptive unsupervised subspace learning for dimensionality reduction.

Weizhi Xiong, Guolin Yu, Jun Ma, Sheng Liu

Eng. Appl. Artif. Intell., 2024

Fast sparse twin learning framework for large-scale pattern classification.

Haoyu Wang, Guolin Yu, Jun Ma

Eng. Appl. Artif. Intell., 2024

2023

A generalized adaptive robust distance metric driven smooth regularization learning framework for pattern recognition.

Jun Ma, Guolin Yu

Signal Process., October, 2023

A Novel Robust Metric Distance Optimization-Driven Manifold Learning Framework for Semi-Supervised Pattern Classification.

Bao Ma, Jun Ma, Guolin Yu

Axioms, August, 2023

Distance Metric Optimization-Driven Neural Network Learning Framework for Pattern Classification.

Yimeng Jiang, Guolin Yu, Jun Ma

Axioms, August, 2023

Safe semi-supervised learning for pattern classification.

Jun Ma, Guolin Yu, Weizhi Xiong, Xiaolong Zhu

Eng. Appl. Artif. Intell., May, 2023

Hessian scatter regularized twin support vector machine for semi-supervised classification.

Guolin Yu, Jun Ma, Chenzhen Xie

Eng. Appl. Artif. Intell., March, 2023

Capped L2, p-Norm Metric Based on Robust Twin Support Vector Machine with Welsch Loss.

Haoyu Wang, Guolin Yu, Jun Ma

Symmetry, 2023

Robust projection twin extreme learning machines with capped L1-norm distance metric.

Yang Yang, Zhenxia Xue, Jun Ma, Xia Chang

Neurocomputing, 2023

2022

Lagrangian Regularized Twin Extreme Learning Machine for Supervised and Semi-Supervised Classification.

Jun Ma, Guolin Yu

Symmetry, 2022

Capped Linex Metric Twin Support Vector Machine for Robust Classification.

Yifan Wang, Guolin Yu, Jun Ma

Sensors, 2022

Regularized twin minimax probability machine for pattern classification and regression.

Jun Ma, Guolin Yu

Eng. Appl. Artif. Intell., 2022

2021

Robust supervised and semi-supervised twin extreme learning machines for pattern classification.

Jun Ma, Liming Yang

Signal Process., 2021

Adaptive robust learning framework for twin support vector machine classification.

Jun Ma, Liming Yang, Qun Sun

Knowl. Based Syst., 2021

Retraction notice to "A novel twin minimax probability machine for classification and regression" [Knowl.-Based Syst. 196 (2020) 105703].

Jun Ma, Jumei Shen

Knowl. Based Syst., 2021

2020

Supervised and semi-supervised twin parametric-margin regularized extreme learning machine.

Jun Ma

Pattern Anal. Appl., 2020

Twin minimax probability extreme learning machine for pattern recognition.

Jun Ma, Liming Yang, Yakun Wen, Qun Sun

Knowl. Based Syst., 2020

Fisher-regularized supervised and semi-supervised extreme learning machine.

Jun Ma, Yakun Wen, Liming Yang

Knowl. Inf. Syst., 2020

Capped L1-norm distance metric-based fast robust twin bounded support vector machine.

Jun Ma, Liming Yang, Qun Sun

Neurocomputing, 2020

Projection multi-birth support vector machinea for multi-classification.

Yakun Wen, Jun Ma, Chao Yuan, Liming Yang

Appl. Intell., 2020

Capped L₁-norm distance metric-based fast robust twin extreme learning machine.

Jun Ma

Appl. Intell., 2020

2019

Lagrangian supervised and semi-supervised extreme learning machine.

Jun Ma, Yakun Wen, Liming Yang

Appl. Intell., 2019

Sparse Twin Extreme Learning Machine With $\varepsilon$ -Insensitive Zone Pinball Loss.

Jumei Shen, Jun Ma

IEEE Access, 2019

Adaptive Safe Semi-Supervised Extreme Machine Learning.

Jun Ma, Chao Yuan

IEEE Access, 2019