联系方式：zoubin0502@hubu.edu.cn
1． Bin Zou, Luoqing Li. The performance bounds of learning machines based on exponentially strongly mixing sequence, Journal of Computer and Mathematics
with Applications, 2007, 53:1050-1058.
2. Bin Zou, Hai Zhang, Zongben Xu. Learning from uniformly ergodic Markov chains,Journal of Complexity, 2009, 25:188-200.
3. Bin Zou, Luoqing Li，Zongben Xu. The generalization performance of ERM algorithm with strongly mixing observations, Machine Learning, 2009,75(3):275-295.
4. Bin Zou, Rong Chen, Zongben Xu. Learning performance of Tikhonov regularization algorithm with geometrically beta-mixing observations, Journal of Statistical
Planning and Inference, 2011, 141:1077-1087.
5. Bin Zou, Zongben Xu, Xiangyu Chang. Generalization bounds of ERM algorithm with V-geometrically ergodic Markov chains, Advances in Computational Mathematics, 2012, 36(1): 99-114.
6. Bin Zou, Luoqing Li, Zongben Xu. Generalization performance of least-square regularized regression algorithm with Markov chain samples, Journal of Mathematical Analysis and Applications, 2012, 388(1): 333-343.
7. Bin Zou, Luoqing Li, Zongben Xu, Tao Luo, Yuan Yan Tang, The generalization performance of Fisher linear discriminant based on Markov sampling, IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(2): 288-300.
8. Bin Zou, Zhiming Pen, Zongben Xu, The learning performance of support vector machine classification based on Markov sampling, Science in China: Information Science, 2013, 56:032110(16).
9. Bin Zou, Yuan Yan Tang, Zongben Xu, Luoqing Li, Jie Xu, Yang Lu, The generalization performance of regularized regression algorithms based on Markov sampling, IEEE Transactions on Cybernetics, 2014, 44(9):1497-1507.
10. Jie Xu, Yuan Yan Tang, Bin Zou, Zongben Xu, Luoqing Li, Yang Lu, Generalization performance of Gaussian kernels SVMC based on Markov sampling, Neural Networks, 2014, 53:40-51.
11. Bin Zou, Zongben Xu, Jie Xu, Generalization bounds of ERM algorithm with Markov chain samples, Acta Mathematicae Applicatae Sinica, 2014, 30(1): 223-238.
12. Jie Xu, Yuan Yan Tang, Bin Zou, Zongben Xu, Luoqing Li, Yang Lu, Baochang Zhang, The Generalization Ability of SVM Classification based on Markov Sampling,IEEE Transactions on Cybernetics. 2015, 45(6):1169-1179.
13. Jie Xu, Yuan Yan Tang, Bin Zou, Zongben Xu, Luoqing Li, Yang Lu, TheGeneralization Ability of Online SVM Classification Based on Markov Sampling,IEEE Transactions on Neural Networks and Learning Systems, 2015,26(3):628-639.
14. Tieliang Gong,Bin Zou,Zongben Xu, Learning with l_{1} regularizer based onMarkov resampling,IEEE Transactions on Cybernetics,2016,46(5):1189-1201.
15. Bin Zou, Chen Xu, Yuan Yan Tang, Jie Xu, Xingge You, K-times Markov samplingfor SVMC,IEEE Transactions on Neural Networks and Learning Systems,2018,9(4):1328-1341.
16. Jie Xu,Chen Xu,Bin Zou,Yuan Yan Tang,Jiangtao Peng,Xinge You,Incremental SVMlassification based on Markov sampling, IEEE Transactions on Systems, Man andCybernetics: Systems,2019,49(11): 2230-2241.
17. Luoqing Li, Weifu Li, Bin Zou, Yulong Wang, Yuan Yan Tang, Hua Han, Learningwith coefficient-based regularized regression on Markov resampling, IEEE Trans.0n Neural Networks and Learning Systems,2018,29(9):4166-4176.
18. Weijian Chen, Chen Xu, Bin Zou, Huidong Jin, Jie Xu,Kernelized elastic netregularization based on Markov selective sampling, Knowledge-Based Systems,2019,163:57-68.
19. Xi Jing, Bin Zou,Chan Wang,Kaifeng Rao,Xiaowen Tang, Carbon emission allocationin a Chinese province-level region based on two-stage network structures,Sustainability,2019,11(5):1369.
20. Hongwei Jiang, Bin Zou,Chen Xu,Jie Xu,Yuan Yan Tang,SVM-Boosting based on Markovresampling:theory and algorithm, Neural Networks,2020,131:276-290.
21. 王婵，邹斌，金茜，唐孝文，谢启维，陈维国，基于两阶段ＤＥＡ方法的中国省际电力能源利用效率评估，数学的认识与实践，2020，50(2):150-161.
22. Jingjing Zeng, Bin Zou, Yimo Qin, Qian Chen, Jie Xu, Lei Yin, Hongwei Jiang,Generalization ability of online pairwise support vector machine, Journal ofMathematical Analysis and Applications, 2021,497(2):124914.
23. Jingjing Zeng, Yuze Duan, Desheng Wang, Bin Zou, Yue Yin,Jie Xu,Generalizationperformance of Lagrangian support vector machine based on Markov sampling,Journal of Statistical Planning and Inference,2021,214:89-104.
1. 国家自然科学基金面上项目“基于非独立同分布数据的机器学习理论及其应用”(NO:61070225), 25 万元, 2011 年 1 月-2013 年 12 月, 已结题.
2. 湖北省自然科学基金重点项目 “基于机器学习的海量数据挖掘及应用” (NO：2011CDA003), 10 万元, 2012 年 1 月-2014 年 12 月, 已结题.
3. 国家自然科学基金面上项目 “基于马氏抽样的机器学习理论与算法研究” (NO:61370002), 62 万元, 2014 年 1 月-2017 年 12 月， 已结题.
4. 国家自然科学基金面上项目“大数据环境下基于马氏抽样的分布式学习的理论与算法研究”(NO: 61772011), 53 万元, 2018 年 1 月-2021 年 12 月， 在研中.
1. 基于非独立同分布数据的机器学习理论和算法, 获 2020 年湖北省自然科学二等奖(排名第一).
