| Using clustering analysis to improve semi-supervised classification H Gan, N Sang, R Huang, X Tong, Z Dan Neurocomputing 101, 290-298, 2013 | 160 | 2013 |
| Improving motor imagery practice with synchronous action observation in stroke patients Y Sun, W Wei, Z Luo, H Gan, X Hu Topics in Stroke Rehabilitation 23 (4), 245-253, 2016 | 112 | 2016 |
| Safety-aware graph-based semi-supervised learning H Gan, Z Li, W Wu, Z Luo, R Huang Expert Systems with Applications 107, 243-254, 2018 | 37 | 2018 |
| An entropy fusion method for feature extraction of EEG S Chen, Z Luo, H Gan Neural Computing and Applications 29, 857-863, 2018 | 36 | 2018 |
| Deep learning-based carotid plaque segmentation from B-mode ultrasound images R Zhou, MR Azarpazhooh, JD Spence, S Hashemi, W Ma, X Cheng, ... Ultrasound in medicine & biology 47 (9), 2723-2733, 2021 | 32 | 2021 |
| Safe semi-supervised extreme learning machine for EEG signal classification Q She, B Hu, H Gan, Y Fan, T Nguyen, T Potter, Y Zhang IEEE Access 6, 49399-49407, 2018 | 29 | 2018 |
| Local homogeneous consistent safe semi-supervised clustering H Gan, Y Fan, Z Luo, Q Zhang Expert Systems with Applications 97, 384-393, 2018 | 29 | 2018 |
| Confidence-weighted safe semi-supervised clustering H Gan, Y Fan, Z Luo, R Huang, Z Yang Engineering Applications of Artificial Intelligence 81, 107-116, 2019 | 26 | 2019 |
| On using supervised clustering analysis to improve classification performance H Gan, R Huang, Z Luo, X Xi, Y Gao Information Sciences 454, 216-228, 2018 | 24 | 2018 |
| Self-training-based face recognition using semi-supervised linear discriminant analysis and affinity propagation H Gan, N Sang, R Huang JOSA A 31 (1), 1-6, 2014 | 23 | 2014 |
| Manifold regularized semi-supervised Gaussian mixture model H Gan, N Sang, R Huang JOSA A 32 (4), 566-575, 2015 | 22 | 2015 |
| Dual learning-based safe semi-supervised learning H Gan, Z Li, Y Fan, Z Luo IEEE Access 6, 2615-2621, 2017 | 19 | 2017 |
| Towards designing risk-based safe laplacian regularized least squares H Gan, Z Luo, Y Sun, X Xi, N Sang, R Huang Expert Systems with Applications 45, 1-7, 2016 | 18 | 2016 |
| Safe Semi-Supervised Fuzzy -Means Clustering H Gan IEEE Access 7, 95659-95664, 2019 | 16 | 2019 |
| Semi-supervised kernel minimum squared error based on manifold structure H Gan, N Sang, X Chen Advances in Neural Networks–ISNN 2013: 10th International Symposium on …, 2013 | 14 | 2013 |
| An improved self-supervised learning for EEG classification Y Ou, S Sun, H Gan, R Zhou, Z Yang Math. Biosci. Eng 19 (7), 6907-6922, 2022 | 13 | 2022 |
| Joint exploring of risky labeled and unlabeled samples for safe semi-supervised clustering L Guo, H Gan, S Xia, X Xu, T Zhou Expert Systems with Applications 176, 114796, 2021 | 13 | 2021 |
| Scale‐Dependent Signal Identification in Low‐Dimensional Subspace: Motor Imagery Task Classification Q She, H Gan, Y Ma, Z Luo, T Potter, Y Zhang Neural Plasticity 2016 (1), 7431012, 2016 | 13 | 2016 |
| Image-dehazing method based on the fusion coding of contours and colors M Tan, T Fang, Y Fan, W Wu, Q She, H Gan IEEE Access 7, 147857-147871, 2019 | 12 | 2019 |
| A risk degree-based safe semi-supervised learning algorithm H Gan, ZZ Luo, M Meng, Y Ma, Q She International Journal of Machine Learning and Cybernetics 7, 85-94, 2016 | 12 | 2016 |
Nong SangHuazhong University of Science and Technology在 hust.edu.cn 的电子邮件经过验证
