Fengzhen Tang
Fengzhen Tang
Shenyang Institution of Automation Chinese Academy of Science
Geverifieerd e-mailadres voor sia.cn
Geciteerd door
Geciteerd door
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
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
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
Feature selection with kernelized multi-class support vector machine
Y Guo, Z Zhang, F Tang
Pattern Recognition 117, 107988, 2021
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
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
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
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
Parameters optimization in SVM based-on ant colony optimization algorithm
XY Liu, HY Jiang, FZ Tang
Advanced materials research 121, 470-475, 2010
Learning the deterministically constructed echo state networks
F Tang, P Tiňo, H Chen
2014 International Joint Conference on Neural Networks (IJCNN), 77-83, 2014
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
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), 013026-013026, 2019
Support Vector Ordinal Regression using Privileged Information.
F Tang, P Tino, PA Gutiérrez, H Chen
ESANN, 2014
A novel oversampling technique based on the manifold distance for class imbalance learning
Y Guo, B Jiao, L Yang, J Cheng, S Yang, F Tang
International Journal of Bio-Inspired Computation 18 (3), 131-142, 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
Nonstationary fuzzy neural network based on FCMnet clustering and a modified CG method with Armijo-type rule
B Zhang, X Gong, J Wang, F Tang, K Zhang, W Wu
Information Sciences 608, 313-338, 2022
Machine-Learning-Based Olfactometry: Odor Descriptor Clustering Analysis for Olfactory Perception Prediction of Odorant Molecules
L Shang, C Liu, F Tang, B Chen, L Liu, K Hayashi
Model learning based on grid cell representations
G Huang, B Si, F Tang
2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1032 …, 2017
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–20