Fengzhen Tang

According to our database, Fengzhen Tang authored at least 29 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction.

Zixin Zhong, Liyan Song, Fengzhen Tang, Bo Yuan

Proceedings of the International Joint Conference on Neural Networks, 2024

Time Window Optimization for Riemannian Geometry-based Motor Imagery EEG Classification.

Fanbo Zhuo, Bo Lv, Fengzhen Tang

Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024

2023

Dimension selection for EEG classification in the SPD Riemannian space based on PSO.

Zirui Zhang, Yinan Guo, Fengzhen Tang

Knowl. Based Syst., November, 2023

Vibration optimization of cantilevered bistable composite shells based on machine learning.

Chenchen Wu, Ruming Zhang, Fengzhen Tang, Mengling Fan

Eng. Appl. Artif. Intell., November, 2023

Knowledge-based hybrid connectionist models for morphologic reasoning.

Kai He, Wenxue Wang, Gang Li, Peng Yu, Fengzhen Tang, Ning Xi, Lianqing Liu

Mach. Vis. Appl., March, 2023

Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold.

Fengzhen Tang, Peter Tino, Haibin Yu

IEEE Trans. Cybern., 2023

Prototype based linear sub-manifold learning.

Mengling Fan, Fengzhen Tang, Xingang Zhao

Proceedings of the International Joint Conference on Neural Networks, 2023

2022

Riemannian dynamic generalized space quantization learning.

Mengling Fan, Fengzhen Tang, Yinan Guo, Xingang Zhao

Pattern Recognit., 2022

Nonstationary fuzzy neural network based on FCMnet clustering and a modified CG method with Armijo-type rule.

Bingjie Zhang, Xiaoling Gong, Jian Wang, Fengzhen Tang, Kai Zhang, Wei Wu

Inf. Sci., 2022

A transfer weighted extreme learning machine for imbalanced classification.

Yinan Guo, Botao Jiao, Ying Tan, Pei Zhang, Fengzhen Tang

Int. J. Intell. Syst., 2022

2021

Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices.

Fengzhen Tang, Mengling Fan, Peter Tiño

IEEE Trans. Neural Networks Learn. Syst., 2021

Feature selection with kernelized multi-class support vector machine.

Yinan Guo, Zirui Zhang, Fengzhen Tang

Pattern Recognit., 2021

Probabilistic learning vector quantization on manifold of symmetric positive definite matrices.

Fengzhen Tang, Haifeng Feng, Peter Tino, Bailu Si, Daxiong Ji

Neural Networks, 2021

A novel oversampling technique based on the manifold distance for class imbalance learning.

Yinan Guo, Botao Jiao, Lingkai Yang, Jian Cheng, Shengxiang Yang, Fengzhen Tang

Int. J. Bio Inspired Comput., 2021

2020

NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation.

Taiping Zeng, Fengzhen Tang, Daxiong Ji, Bailu Si

Neural Networks, 2020

2019

Learning joint space-time-frequency features for EEG decoding on small labeled data.

Dongye Zhao, Fengzhen Tang, Bailu Si, Xisheng Feng

Neural Networks, 2019

Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback-Leibler divergence.

Min Jiang, Sanming Song, Fengzhen Tang, Yiping Li, Jian Liu, Xisheng Feng

J. Electronic Imaging, 2019

Unsupervised Feature Learning for Visual Place Recognition in Changing Environments.

Dongye Zhao, Bailu Si, Fengzhen Tang

Proceedings of the International Joint Conference on Neural Networks, 2019

2018

Group feature selection with multiclass support vector machine.

Fengzhen Tang, Lukás Adam, Bailu Si

Neurocomputing, 2018

2017

Ordinal regression based on learning vector quantization.

Fengzhen Tang, Peter Tiño

Neural Networks, 2017

Model learning based on grid cell representations.

Guanwen Huang, Bailu Si, Fengzhen Tang

Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics, 2017

A prey-predator model for efficient robot tracking.

Fengzhen Tang, Bailu Si, Daxiong Ji

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

2015

Kernel methods for time series data.

Fengzhen Tang

PhD thesis, 2015

The Benefits of Modeling Slack Variables in SVMs.

Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen

Neural Comput., 2015

Model Metric Co-Learning for Time Series Classification.

Huanhuan Chen, Fengzhen Tang, Peter Tiño, Anthony G. Cohn, Xin Yao

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

2014

Learning the deterministically constructed Echo State Networks.

Fengzhen Tang, Peter Tiño, Huanhuan Chen

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Support Vector Ordinal Regression using Privileged Information.

Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

2013

Model-based kernel for efficient time series analysis.

Huanhuan Chen, Fengzhen Tang, Peter Tiño, Xin Yao

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

2010

Liver cancer identification based on PSO-SVM model.

Huiyan Jiang, Fengzhen Tang, Xiyue Zhang

Proceedings of the 11th International Conference on Control, 2010