76. Dong Li, Shuisheng Zhou, Tieyong Zeng, and Raymond H. Chan. Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(Accepted).
75. Zhuan Zhang, Shuisheng Zhou,Adaptive proximal SGD based on new estimating sequences for sparser ERM, Information Sciences, Volume 638, 2023, 118965
74. Xiling Liu, Shuisheng Zhou. Quality-related Fault Detection Based on Approximate Kernel Partial Least Squares Method, Joural of Grid Computing, vol. 21, Article number: 29, 2023.
73. 孟田田;周水生;田昕润. 基于l2,0范数稀疏性和模糊相似性的图优化无监督组特征选择方法, 模式识别与人工智能, 2023, 36(01):34-48.
72. Cui Fu, Shuisheng Zhou, Yuxue Chen, Li Chen, Banghe Han. A risk-averse learning machine via variance-dependent penalization, Pattern Recognition Letters, Volume 171, July 2023, Pages 116-123.
71. Xinrun Tian; Shuisheng Zhou; Tiantian Meng. Robust Matrix Completion Method Based on TNNR and Total Row Difference for Recovering Optical Image, IEEE Journal on Miniaturization for Air and Space Systems, Vol. 4, No. 2, 105-113.
70. Junna Zhang, Shuisheng Zhou, Cui Fu, Feng Ye. Fast newton method to solve KLR based on multilevel circulant matrix with log-linear complexity, Applied Intelligence, 53, pages21407–21421 (2023).
69. Dong Li, Shuisheng Zhou, and Witold Pedrycz. Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling, IEEE Trans. on Knowledge and Data Engineering, 2023, Volume: 35, Issue: 12, pp.12337 - 12349 (CODE).
68. Cui Fu, Shuisheng Zhou, Junna Zhang, Banghe Han, Yuxue Chen, Feng Ye. Risk-Averse support vector classifier machine via moments penalization. Int. J. Mach. Learn. & Cyber. Vol.13, p.3341–3358,2022. https://doi.org/10.1007/s13042-022-01598-4.
67. Ting Yang, Shuisheng Zhou, Zhuan Zhang. The k-sparse LSR for subspace clustering via 0-1 integer programming, Signal Processing, Volume 199, 2022, 108622, https://doi.org/10.1016/j.sigpro.2022.108622.
66. Yuxue Chen, Shuisheng Zhou, Ximin Zhang, Dong Li, Cui Fu. Improved fuzzy c-means clustering by varying the fuzziness parameter,
Pattern Recognition Letters, Volume 157, 2022, Pages 60-66, https://doi.org/10.1016/j.patrec.2022.03.017.
65. Zhuan Zhang, Shuisheng Zhou, Dong Li, Ting Yang. Riemannian proximal stochastic gradient descent for sparse 2DPCA, Digital Signal Processing, Volume 122, 2022, 103320, https://doi.org/10.1016/j.dsp.2021.103320.
64 Zhuan Zhang, Shuisheng Zhou, Ting Yang, Junna Zhang. Faster doubly stochastic functional gradient by gradient preconditioning for scalable kernel methods. Appl Intell 52, 7091–7112 (2022). https://doi.org/10.1007/s10489-021-02618-6.
63. Jiajun Ma, Shuisheng Zhou. Discriminative least squares regression for multiclass classification based on within-class scatter minimization. Appl Intell 52, 622–635 (2022). https://doi.org/10.1007/s10489-021-02258-w.
62. 康倩;周水生. 光滑有下界的奖惩结合损失函数的最大间隔双球模型, 模式识别与人工智能, 2021, 34(10):885-897.
61. Shuisheng Zhou, Wendi Zhou. Unified SVM algorithm based on LS-DC loss, Machine Learning, 112, pages2975–3002 (2023), Unified SVM algorithm based on LS-DC loss | SpringerLink, pdf.
60. Li Chen, Shuisheng Zhou, Jiajun Ma, Mingliang Xu. Fast Kernel $k$-means Clustering Using Incomplete Cholesky Factorization. Applied Mathematics and Computation, 2021, Volume 402, 126037. www.sciencedirect.com/science/article/pii/S0096300321000850
59. Jiajun Ma, Shuisheng Zhou, Dong Li. Robust Multiclass Least Squares Support Vector Classifier with Optimal Error Distribution, Knowledge-based Systems, Volume 215, 106652,2021. https://doi.org/10.1016/j.knosys.2020.106652, pdf.
58. 平瑞, 周水生, 李东. 高度不平衡数据的代价敏感随机森林分类算法,模式识别与人工智能, 2020, 33(3):249-257, DOI:10.16451/j.cnki.issn1003-6059.202003006.
57. Zhuan Zhang, Shuisheng Zhou. Gradient preconditioned mini-batch SGD for ridge regression, Neurocomputing, 2020, 413: 284-293. https://doi.org/10.1016/j.neucom.2020.06.092.
56. Jiajun Ma, Shuisheng Zhou. Metric learning-guided k nearest neighbor multilabel classifier, Neural Computing and Applications, 2020. https://doi.org/10.1007/s00521-020-05134-9.
55. Shuisheng Zhou, Dong Li, Zhuan Zhang, and Rui Ping. New Membership Scaling Fuzzy C-Means Clustering Algorithm, IEEE Trans. on Fuzzy Systems, 2020, DOI: 10.1109/TFUZZ.2020.3003441. (code)
54. Xiling Liu Shuisheng Zhou, Approximate kernel partial least squares. Annals of Mathematics and Artificial Intelligence(2020), 27 March 2020, https://doi.org/10.1007/s10472-020-09694-3.
53. Li Chen, Shuisheng Zhou, Jiajun Ma. Stable sparse subspace embedding for dimensionality reduction, Knowledge-Based Systems, Volume 195, 11 May 2020, https://doi.org/10.1016/j.knosys.2020.105639
52. 安亚利,周水生,陈丽,王保军. 鲁棒支持向量机及其稀疏算法, 西安电子科技大学学报 Vol.46, No.1,pp.64-72,2019. (doi: 10.19665/j.issn1001-2400.2019.01.011)(EI:20191606810422).
51. Zhou, Shuisheng, Zhang, Danqing. Bilateral Angle 2DPCA for Face Recognition, IEEE SIGNAL PROCESSING LETTERS, vol.26, No.2, pp.317-321, 2019. (WOS:000455914600008)
50. Ma, Jiajun, Zhou, Shuisheng, etc. A sparse robust model for large scale multi-class classification based on K-SVCR, PATTERN RECOGNITION LETTERS, vol.117, pp.16-23, 2019. (WOS:000455196900003)
49. Shuisheng Zhou, Baojun Wang, Li Chen.High precision approximate analytical solutions to ODE using LS-SVM, The Journal of China Universities of Posts and Telecommunications, 25(4):94-102,2018. (EI:20185206315686)
48. Li Chen, Shuisheng Zhou, Zhuan Zhang. SVRG for a non-convex problem using graduated optimizatin algorithm. Journal of Intelligent & Fuzzy Systems, Vol. 34, No. 1, pp.153-165, 2018.
47. Li Chen, Shuisheng Zhou. Sparse algorithm for robust LSSVM in primal space, Neurocomputing, Vol 275(31):2880-2891, 2018. (pdf, code)
46. Zhou, Shuisheng, Liu, Mengnan. A new sparse LSSVM method based the revised LARS, 2017 International Conference on Machine Vision and Information Technology, CMVIT 2017, March 14, 2017, Pages: 46-51. (EI: 20171503564676)
45. Li Chen,Shuisheng Zhou, et al. Fast kernel fuzzy c-means algorithms based on difference of convex programming. ICNC-FSKD,2016,Agu. pp.1090-1095.
44. 周水生等. 基于Cholesky分解的K2DPCA人脸识别研究, 投稿《系统工程与电子技术.》 2015 (in Chinese). (pdf, code)
43. Shuisheng Zhou. Sparse LSSVM in primal using Cholesky Factorization for large scale problems, Submitted to IEEE Trans. NNLS(Conditional accepted). 2015. (pdf, code)
42. Manfred K.Warmuth, WojciechKotłowskib, ShuishengZhou. Kernelization of matrix updates, when and how? Theoretical Computer Science, 2014, pp.159-178. DOI: 10.1016/j.tcs.2014.09.031.
41. 史加荣、周水生、郑秀云 ，多线性鲁棒主成分分析 ，电子学报, 08期, pp 1480-1486, 2014/8/15(in Chinese).
40. 赵扬扬、周水生、武亚静 ，一种用于人脸识别的非迭代GLRAM算法 ，西安电子科技大学学报, 02期, pp 144-150, 2014(in Chinese).
39. Shuisheng Zhou. Which is better? Regularization in RKHS vs Rm for RSVMs, Statistics, Optimization and Information Computing, 1 (1), 82-106, 2013. DOI: 10.19139/soic.v1i1.27. ( pdf, code )
38. Shuisheng Zhou, Jiangtao Cui, et al. New Smoothing SVM Algorithm with Tight Error Bound and Efficient Reduced Techniques. Computational Optimization and Applications, 56(3), 599-617, 2013. (pdf, code)
37. Shuisheng Zhou, Feng Ye et al. Exact Sparse LS-SVM. Proceedings of the 5th International Conference on Optimization and Control with Applications (OCA2012), pp143-148, Beijing, China, December 4-8, 2012.
36. Warmuth, Manfred K,Kotowski, Wojciech; Zhou, Shuisheng. Kernelization of matrix updates, when and how? Algorithmic Learning Theory - 23rd International Conference, ALT 2012,v. 7568 LNAI, p350-364, 2012.
35. Yinli Dong, Shuisheng Zhou. SVM Regularizer Models on RKHS vs. on Rm, LNCS 7389(ICIC2012 ), pp. 103-111, 2012(EI/ISTP).
34. 董银丽,周水生,高艳.新的软间隔 AdaBoost弱分类器权重调整算法,计算机工程,2012,38(7):125-127(in Chinese).
33. Shuisheng Zhou, Manfred K. Warmuth, Yinli Dong and Feng Ye. New Combination Coefficients for AdaBoost Algorithms, ICNC 2010, pp:3194-3198(EI/ISTP).
32. Jiangtao Cui, Zhiyong An, Yong Guo, Shuisheng Zhou. Efficient nearest neighbor query based on extended B-tree in high-dimensional space. Pattern Recognition Letters, 2010, 31(12):1740-1748SCI/EI).
31. Shuisheng Zhou, Hongwei Liu, Feng Ye. Variant of Gaussian Kernel and Parameter Setting Method for Nonlinear SVM. Neurocomputing, 2009, 72(13-15):2931-2937(SCI/EI).
30. Shuisheng Zhou, Hongwei Liu, Lihua Zhou. A New Iterative Training SVM. Optimization Method and Software,2009,24(6): 913-932 (SCI/EI).
29. Tiantian Chang, Hongwei Liu, Shuisheng Zhou. Large scale classification with local diversity AdaBoost SVM algorithm, JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 20(6):1344-1350, 2009/12(SCI/EI).
28. 赵玲玲,周水生, 王雪岩. 基于集成算法的半监督学习, 信号处理, 2009, 25(8A):320-323.(in Chinese)
27. Feng Ye, Hongwei Liu, Shuisheng Zhou, Sanyang Liu. A smoothing trust-region Newton-CG method for minimax problem. Applied Mathematics And Computation, 2008, 199(2):581-589. (SCI/EI).
26. B. S. Goh, Feng Ye, Shuisheng Zhou. Steepest Descent Algorithms in Optimization with Good Convergence Properties, 20th Chinese Control and Decision Conference, 2008/7/2, pp 1526-1530.(EI/ISTP)
25. Shuisheng Zhou, Hongwei Liu, Lihua Zhou. Semismooth Newton Support Vector Machine. Pattern Recognition Letters, 2007, 28(15): 2054-2062. (SCI/EI).
24. Jiangtao Cui, Shuisheng Zhou, Junding Sun. Efficient high-dimensional indexing by sorting principal component. Pattern Recognition Letters. 2007, 28(16): 2412-2418.(SCI/EI).
23. Jiangtao Cui, Shuisheng Zhou, Shan Zhao. PCR-tree: A Compression-based index structure for similarity searching in high-dimensional image databases, FSKD 2007, pp 395-400, 2007/8/24 (EI/ISTP) .
22. Shuisheng Zhou, Hongwei Liu, Feng Ye. The Variant of Gaussian Kernel and Its Model Selection Method, 3ed international conference on Natural Computation, Haikou, China, 2007, August, pp683-687.( EI/ISTP).
21. 王钰,周水生, 刘红卫. 采用双目标优化的核参数选择方法, 电光与控制, 2007, 14(06):197-201(in Chinese).
20. Shuisheng Zhou, Hongwei Liu, Jiangtao Cui, Lihua Zhou. Exact Semismooth Newton SVM. SLNSC 4221: Advance in Natural Computation,2006, 9(SCI/EI/ISTP).
19. Shuisheng Zhou, Weiwei Wang, Lihua Zhou. A New Technique for Generalized Learning Vector Quantization Algorithm. Image and Vision Computing, 2006, Vo.24, No. 7, 649-655 (SCI/EI).
18. 周水生, 周利华. 共轭梯度型支撑向量机(CGSVM). 模式识别与人工智能, 2006, 19,2,129-136. (EI)(in Chinese)
17. 周水生, 詹海生, 周利华. 训练支持向量机的Huber近似算法. 计算机学报, 2005, 28, 10, 1664-1670.(EI)(in Chinese)
16. 周水生, 周利华. 训练支持向量机的低维Newton算法, 系统工程与电子技术, 2004, 26, 9, 1315-1318. (EI)(in Chinese)
15. 张惠娟, 周水生, 周利华. 一种混合实时任务系统的公平调度算法. 西安电子科技大学学报, 2004, 31, 2, 272-275. (EI)(in Chinese)
14. Shuisheng Zhou, Lihua Zhou. A new measure to improve the performance of the LVQ algorithms, Picture Coding Symposium. Saint Malo, France, 2003, 4, 115-118. (EI).
13. 周水生,容晓锋,周利华, 训练支持向量机的极大熵方法. 信号处理, 2003,19, 6, 595-599.(in Chinese)
12. 周水生, 张惠娟, 崔江涛, 周利华.一种提高学习向量量化算法的新方法. 中国图像图形学报. 2003, 8, A, 59-63.(In Chinese)
11. 周水生, 周利华. 修正的广义学习向量量化算法. 计算机工程, 2003, 29, 13, 34-36. (EI)(in Chinese)
10. 周水生, 容晓峰, 周利华. 计算两个凸多面体间距离的一个新算法. 苏州科技学院学报. 2003,20,2, 11-16.
9. 崔江涛, 周水生, 周利华. 高维图像数据库中一种新的多分辨率特征匹配算法. 中国图像图形学报. 2003, 8, A, 488-491.(in Chinese)
8. Shuisheng Zhou, Lihua Zhou, Weiguang Liu, A new generalized learning vector quantization algorithm. SPIE 2002, Vol 4875: 111-117. (EI, ISTP).
7. 周水生，周利华. 确定最优分类超平面的新算法. 西安电子科技大学学报. 2002, 29, 6, 791-795. (EI)(in Chinese)
6. 周水生, 容晓峰, 周利华. 判断两个凸多面体相交的简单算法. 宝鸡文理学院学报, 2002, 22, 1, 24-26.(in Chinese)
5. 赵天绪, 郝跃，周水生. VLSI冗余单元最优分配的遗传算法. 电子与信息学报. 2001, 23, 1, 96-99.(Chinese)
4. 刘红英; 刘三阳; 周水生. 两层广义线性规划. 系统工程学报. 2000, 15,2, 131-135.(Chinese)
3. 周水生,刘三阳,刘红英.价格控制问题及其推广形式的罚函数法. 系统工程学报, 1999,14,2,156-161.(in Chinese)
2. 周水生, 刘三阳. 价格控制问题的基本性质. 应用数学与计算数学学报. 1998,12,2,53-58.(in Chinese)
1. 周水生, 刘三阳. 线性-二次二层规划问题的性质及全局算法. 西安电子科技大学学报,1998,25, 1, 24-27.(in Chinese)