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Fengzhen Tang

Showing results (1-10 of 19) with videos related to

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Neural Networks : the Official Journal of the International Neural Network Society|May 30, 2017
Ordinal regression based on learning vector quantizationFengzhen Tang, Peter Tiňo
IEEE Transactions on Neural Networks and Learning Systems|March 24, 2020
Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD MatricesFengzhen Tang, Mengling Fan, Peter Tino
IEEE Transactions on Cybernetics|June 14, 2022
Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite ManifoldFengzhen Tang, Peter Tino, Haibin Yu
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|March 5, 2025
Time Window Optimization for Riemannian Geometry-based Motor Imagery EEG ClassificationFanbo Zhuo, Bo Lv, Fengzhen Tang
Neural Networks : the Official Journal of the International Neural Network Society|March 18, 2020
NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigationTaiping Zeng, Fengzhen Tang, Daxiong Ji, et al.
Biomimetics (Basel, Switzerland)|October 25, 2024
Brain-Inspired Architecture for Spiking Neural NetworksFengzhen Tang, Junhuai Zhang, Chi Zhang, et al.
Neural Networks : the Official Journal of the International Neural Network Society|March 22, 2019
Learning joint space-time-frequency features for EEG decoding on small labeled dataDongye Zhao, Fengzhen Tang, Bailu Si, et al.
Cognitive Neurodynamics|August 6, 2024
A spatial transformation-based CAN model for information integration within grid cell modulesZhihui Zhang, Fengzhen Tang, Yiping Li, et al.
Cognitive Neurodynamics|June 3, 2024
Modeling the grid cell activity based on cognitive space transformationZhihui Zhang, Fengzhen Tang, Yiping Li, et al.
Neural Computation|March 4, 2015
The benefits of modeling slack variables in SVMsFengzhen Tang, Peter Tiňo, Pedro Antonio Gutiérrez, et al.
Pageof 2

Showing results (1-10 of 19) with videos related to

Sort By:
Pageof 2
Neural Networks : the Official Journal of the International Neural Network Society|May 30, 2017
Ordinal regression based on learning vector quantizationFengzhen Tang, Peter Tiňo
IEEE Transactions on Neural Networks and Learning Systems|March 24, 2020
Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD MatricesFengzhen Tang, Mengling Fan, Peter Tino
IEEE Transactions on Cybernetics|June 14, 2022
Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite ManifoldFengzhen Tang, Peter Tino, Haibin Yu
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|March 5, 2025
Time Window Optimization for Riemannian Geometry-based Motor Imagery EEG ClassificationFanbo Zhuo, Bo Lv, Fengzhen Tang
Neural Networks : the Official Journal of the International Neural Network Society|March 18, 2020
NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigationTaiping Zeng, Fengzhen Tang, Daxiong Ji, et al.
Biomimetics (Basel, Switzerland)|October 25, 2024
Brain-Inspired Architecture for Spiking Neural NetworksFengzhen Tang, Junhuai Zhang, Chi Zhang, et al.
Neural Networks : the Official Journal of the International Neural Network Society|March 22, 2019
Learning joint space-time-frequency features for EEG decoding on small labeled dataDongye Zhao, Fengzhen Tang, Bailu Si, et al.
Cognitive Neurodynamics|August 6, 2024
A spatial transformation-based CAN model for information integration within grid cell modulesZhihui Zhang, Fengzhen Tang, Yiping Li, et al.
Cognitive Neurodynamics|June 3, 2024
Modeling the grid cell activity based on cognitive space transformationZhihui Zhang, Fengzhen Tang, Yiping Li, et al.
Neural Computation|March 4, 2015
The benefits of modeling slack variables in SVMsFengzhen Tang, Peter Tiňo, Pedro Antonio Gutiérrez, et al.
Pageof 2