Fengzhen Tang
Fengzhen Tang
Shenyang Institution of Automation Chinese Academy of Science
Verified email at sia.cn
Cited by
Cited by
Model-based kernel for efficient time series analysis
H Chen, F Tang, P Tino, X Yao
Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013
Model metric co-learning for time series classification
H Chen, F Tang, P Tino, AG Cohn, X Yao
Twenty-fourth international joint conference on artificial intelligence, 2015
Learning joint space–time–frequency features for EEG decoding on small labeled data
D Zhao, F Tang, B Si, X Feng
Neural Networks 114, 67-77, 2019
Group feature selection with multiclass support vector machine
F Tang, L Adam, B Si
Neurocomputing 317, 42-49, 2018
Liver cancer identification based on PSO-SVM model
H Jiang, F Tang, X Zhang
2010 11th International Conference on Control Automation Robotics & Vision …, 2010
Parameters optimization in SVM based-on ant colony optimization algorithm
XY Liu, HY Jiang, FZ Tang
Advanced Materials Research 121, 470-475, 2010
Ordinal regression based on learning vector quantization
F Tang, P Tiňo
Neural Networks 93, 76-88, 2017
The benefits of modeling slack variables in svms
F Tang, P Tiňo, PA Gutiérrez, H Chen
Neural computation 27 (4), 954-981, 2015
NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation
T Zeng, F Tang, D Ji, B Si
Neural Networks 126, 21-35, 2020
Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback–Leibler divergence
M Jiang, S Song, F Tang, Y Li, J Liu, X Feng
Journal of Electronic Imaging 28 (1), 013026, 2019
Learning the deterministically constructed echo state networks
F Tang, P Tiňo, H Chen
2014 International Joint Conference on Neural Networks (IJCNN), 77-83, 2014
Support Vector Ordinal Regression using Privileged Information.
F Tang, P Tino, PA Gutiérrez, H Chen
ESANN, 2014
Feature selection with kernelized multi-class support vector machine
Y Guo, Z Zhang, F Tang
Pattern Recognition 117, 107988, 2021
Unsupervised feature learning for visual place recognition in changing environments
D Zhao, B Si, F Tang
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices
F Tang, M Fan, P Tiňo
IEEE transactions on neural networks and learning systems 32 (1), 281-292, 2020
Model learning based on grid cell representations
G Huang, B Si, F Tang
2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1032 …, 2017
A prey-predator model for efficient robot tracking
F Tang, B Si, D Ji
2017 IEEE International Conference on Robotics and Automation (ICRA), 3568-3574, 2017
Data de-noising based on PCA-KNN algorithm in billet surface temperature measurement
H Jiang, F Tang, L Zou, Y Chen
Appl. Math 7 (2L), 455-458, 2013
Probabilistic learning vector quantization on manifold of symmetric positive definite matrices
F Tang, H Feng, P Tino, B Si, D Ji
Neural Networks 142, 105-118, 2021
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