Publications

2022 and before

• Tang J, Li D, Tian Y. Image classification with multi-view multi-instance metric learning[J]. Expert Systems with Applications, 2022, 189: 116117.(pdf)

• Ma Y, Zhang Q, Li D, et al. Linex support vector machine for large-scale classification[J]. IEEE Access, 2019, 7: 70319-70331.(pdf)
• Li D, Tian Y. Improved least squares support vector machine based on metric learning[J]. Neural Computing and Applications, 2018, 30: 2205-2215.(pdf)
• Tang J, Tian Y, Liu X, Li D. Improved multi-view privileged support vector machine[J]. Neural Networks, 2018, 106: 96-109.
• Tang J, Li D, Tian Y, Liu D. Multi-view learning based on nonparallel support vector machine. Knowledge-Based Systems 158 (2018): 94-108.
• Li, D., & Tian, Y. (2018). Survey and experimental study on metric learning methods. Neural networks, 105, 447-462.(pdf)
• Liu, D., Li, D., Shi, Y., & Tian, Y. (2018). Large-scale linear nonparallel SVMs. Soft Computing, 22, 1945-1957.(pdf)
• Li, D., & Tian, Y. (2017). Global and local metric learning via eigenvectors. Knowledge-Based Systems, 116, 152-162.(pdf)
• Li, D., Tang, J., Tian, Y., & Ju, X. (2017). Multi-view deep metric learning for image classification. In 2017 IEEE International Conference on Image Processing (ICIP) (pp. 4142-4146). IEEE.(pdf)
• Tang, J., Tian, Y., Wu, G., & Li, D. (2017). Stochastic gradient descent for large-scale linear nonparallel SVM. In Proceedings of the International Conference on Web Intelligence (pp. 980-983).
• Li, D., Xu, D., Tang, J., & Tian, Y. (2017). Metric learning for multi-instance classification with collapsed bags. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 372-379). IEEE.(pdf)
• Xu, D., Wu, J., Li, D., Tian, Y., Zhu, X., & Wu, X. (2017). SALE: Self-adaptive LSH encoding for multi-instance learning. Pattern recognition, 71, 460-482.
• Li, D., Zhang, W., Xu, D., & Tian, Y. (2016). Multi-metrics classification machine. Procedia Computer Science, 91, 556-565.
• Jingjing Tang and Dewei Li. “Incorporate Hashing with Multi-view Learning”[C]// 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016: 853-859.
• Li, D., Tian, Y., & Xu, H. (2014, December). Deep twin support vector machine. In 2014 IEEE International Conference on Data Mining Workshop (pp. 65-73). IEEE.
• Li, D., & Tian, Y. (2014). Twin support vector machine in linear programs. Procedia Computer Science, 29, 1770-1778.(pdf)
• Zhang, C., Shao, X., & Li, D. (2013). Knowledge-based support vector classification based on C-SVC. Procedia Computer Science, 17, 1083-1090.
• Zhang, C., Li, D., & Tan, J. (2013). The support vector regression with adaptive norms. Procedia Computer Science, 18, 1730-1736.(pdf)