| 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 | 145 | 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 | 119 | 2019 |
| Feature selection with kernelized multi-class support vector machine Y Guo, Z Zhang, F Tang Pattern Recognition 117, 107988, 2021 | 111 | 2021 |
| Model Metric Co-Learning for Time Series Classification. H Chen, F Tang, P Tino, AG Cohn, X Yao Proceedings of the Twenty-Fourth International Joint Conference on …, 2015 | 74 | 2015 |
| 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 | 44 | 2022 |
| Group feature selection with multiclass support vector machine F Tang, L Adam, B Si Neurocomputing 317, 42-49, 2018 | 43 | 2018 |
| 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 | 38 | 2020 |
| 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 | 37 | 2020 |
| An analysis of deep learning models in SSVEP-based BCI: a survey D Xu, F Tang, Y Li, Q Zhang, X Feng Brain Sciences 13 (3), 483, 2023 | 28 | 2023 |
| Liver cancer identification based on PSO-SVM model H Jiang, F Tang, X Zhang 2010 11th International Conference on Control Automation Robotics & Vision …, 2010 | 21 | 2010 |
| 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 | 16 | 2019 |
| Generalized learning vector quantization with log-Euclidean metric learning on symmetric positive-definite manifold F Tang, P Tiňo, H Yu IEEE Transactions on Cybernetics 53 (8), 5178-5190, 2022 | 14 | 2022 |
| The benefits of modeling slack variables in svms F Tang, P Tiňo, PA Gutiérrez, H Chen Neural computation 27 (4), 954-981, 2015 | 14 | 2015 |
| Parameters optimization in SVM based-on ant colony optimization algorithm XY Liu, HY Jiang, FZ Tang Advanced materials research 121, 470-475, 2010 | 14 | 2010 |
| Ordinal regression based on learning vector quantization F Tang, P Tiňo Neural Networks 93, 76-88, 2017 | 13 | 2017 |
| 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 | 11 | 2021 |
| Vibration optimization of cantilevered bistable composite shells based on machine learning C Wu, R Zhang, F Tang, M Fan Engineering Applications of Artificial Intelligence 126, 107158, 2023 | 9 | 2023 |
| Learning the deterministically constructed echo state networks F Tang, P Tiňo, H Chen 2014 International Joint Conference on Neural Networks (IJCNN), 77-83, 2014 | 9 | 2014 |
| Riemannian dynamic generalized space quantization learning ML Fan, F Tang, Y Guo, X Zhao Pattern Recognition 132, 108932, 2022 | 7 | 2022 |
| A transfer weighted extreme learning machine for imbalanced classification Y Guo, B Jiao, Y Tan, P Zhang, F Tang International Journal of Intelligent Systems 37 (10), 7685-7705, 2022 | 7 | 2022 |
