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ORCIDFengzhen Tang
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2020 – today
- 2024
[c10]Zixin Zhong, Liyan Song, Fengzhen Tang, Bo Yuan:
Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction. IJCNN 2024: 1-10- 2023
- [j17]Chenchen Wu
, Ruming Zhang, Fengzhen Tang, Mengling Fan:
Vibration optimization of cantilevered bistable composite shells based on machine learning. Eng. Appl. Artif. Intell. 126(Part D): 107158 (2023) - [j16]Zirui Zhang, Yinan Guo, Fengzhen Tang:
Dimension selection for EEG classification in the SPD Riemannian space based on PSO. Knowl. Based Syst. 279: 110933 (2023) - [j15]Kai He, Wenxue Wang, Gang Li, Peng Yu, Fengzhen Tang, Ning Xi, Lianqing Liu:
Knowledge-based hybrid connectionist models for morphologic reasoning. Mach. Vis. Appl. 34(2): 29 (2023) - [j14]Fengzhen Tang, Peter Tino, Haibin Yu:
Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold. IEEE Trans. Cybern. 53(8): 5178-5190 (2023) - [c9]Mengling Fan, Fengzhen Tang, Xingang Zhao:
Prototype based linear sub-manifold learning. IJCNN 2023: 1-8 - 2022
- [j13]Yinan Guo, Botao Jiao, Ying Tan, Pei Zhang, Fengzhen Tang:
A transfer weighted extreme learning machine for imbalanced classification. Int. J. Intell. Syst. 37(10): 7685-7705 (2022) - [j12]Bingjie Zhang, Xiaoling Gong, Jian Wang, Fengzhen Tang, Kai Zhang, Wei Wu:
Nonstationary fuzzy neural network based on FCMnet clustering and a modified CG method with Armijo-type rule. Inf. Sci. 608: 313-338 (2022) - [j11]Mengling Fan, Fengzhen Tang, Yinan Guo, Xingang Zhao:
Riemannian dynamic generalized space quantization learning. Pattern Recognit. 132: 108932 (2022) - 2021
- [j10]Yinan Guo, Botao Jiao, Lingkai Yang, Jian Cheng, Shengxiang Yang, Fengzhen Tang:
A novel oversampling technique based on the manifold distance for class imbalance learning. Int. J. Bio Inspired Comput. 18(3): 131-142 (2021) - [j9]Fengzhen Tang, Haifeng Feng, Peter Tino, Bailu Si, Daxiong Ji:
Probabilistic learning vector quantization on manifold of symmetric positive definite matrices. Neural Networks 142: 105-118 (2021) - [j8]Yinan Guo, Zirui Zhang, Fengzhen Tang:
Feature selection with kernelized multi-class support vector machine. Pattern Recognit. 117: 107988 (2021) - [j7]Fengzhen Tang, Mengling Fan, Peter Tiño:
Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices. IEEE Trans. Neural Networks Learn. Syst. 32(1): 281-292 (2021) - [i1]Fengzhen Tang, Haifeng Feng, Peter Tiño, Bailu Si, Daxiong Ji:
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices. CoRR abs/2102.00667 (2021) - 2020
- [j6]Taiping Zeng, Fengzhen Tang, Daxiong Ji, Bailu Si:
NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation. Neural Networks 126: 21-35 (2020)
2010 – 2019
- 2019
- [j5]Min Jiang, Sanming Song, Fengzhen Tang, Yiping Li, Jian Liu, Xisheng Feng:
Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback-Leibler divergence. J. Electronic Imaging 28(1): 013026 (2019) - [j4]Dongye Zhao, Fengzhen Tang, Bailu Si, Xisheng Feng:
Learning joint space-time-frequency features for EEG decoding on small labeled data. Neural Networks 114: 67-77 (2019) - [c8]Dongye Zhao, Bailu Si, Fengzhen Tang:
Unsupervised Feature Learning for Visual Place Recognition in Changing Environments. IJCNN 2019: 1-8 - 2018
- [j3]Fengzhen Tang, Lukás Adam, Bailu Si:
Group feature selection with multiclass support vector machine. Neurocomputing 317: 42-49 (2018) - 2017
- [j2]Fengzhen Tang, Peter Tiño:
Ordinal regression based on learning vector quantization. Neural Networks 93: 76-88 (2017) - [c7]Fengzhen Tang, Bailu Si, Daxiong Ji:
A prey-predator model for efficient robot tracking. ICRA 2017: 3568-3574 - [c6]Guanwen Huang, Bailu Si, Fengzhen Tang:
Model learning based on grid cell representations. ROBIO 2017: 1032-1037 - 2015
- [b1]Fengzhen Tang:
Kernel methods for time series data. University of Birmingham, UK, 2015 - [j1]Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen:
The Benefits of Modeling Slack Variables in SVMs. Neural Comput. 27(4): 954-981 (2015) - [c5]Huanhuan Chen, Fengzhen Tang, Peter Tiño, Anthony G. Cohn, Xin Yao:
Model Metric Co-Learning for Time Series Classification. IJCAI 2015: 3387-3394 - 2014
- [c4]Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen:
Support Vector Ordinal Regression using Privileged Information. ESANN 2014 - [c3]Fengzhen Tang, Peter Tiño, Huanhuan Chen:
Learning the deterministically constructed Echo State Networks. IJCNN 2014: 77-83 - 2013
- [c2]Huanhuan Chen, Fengzhen Tang, Peter Tiño, Xin Yao:
Model-based kernel for efficient time series analysis. KDD 2013: 392-400 - 2010
- [c1]Huiyan Jiang, Fengzhen Tang, Xiyue Zhang:
Liver cancer identification based on PSO-SVM model. ICARCV 2010: 2519-2523
Coauthor Index
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