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论文/著作

 教材专著

3. 译著《凸优化教程》,机械工业出版社,2020. (原著:《Lectures on Convex Optimizaition》,Yurii Nesterov).
2. 《数学规划基础》, 刘红英, 夏勇, 周水生. 北京航空航天大学出版社, 2013. 获北京市2013年精品教材.
1. 《大学生数学手册》, 王金金, 任春丽, 冶继民, 周水生. 西安电子科技大学出版社, 2008.

 

近期发表的部分论文(Google Scholar学术引用统计)

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. https://authors.elsevier.com/a/1cf6ALvMgRCsI
59.  Jiajun Ma, Shuisheng Zhou, Dong Li. Robust Multiclass Least Squares Support Vector Classifier with Optimal Error Distribution, 2020,  Knowledge-based Systems, 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. (pdfcode)

46. Zhou, Shuisheng, Liu, Mengnan. A new sparse LSSVM method based on 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 the 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, 系统工程理论与实践, 2016,36(2):528-535.  (pdf, code)
43. Shuisheng Zhou. Sparse LSSVM in primal using Cholesky Factorization for large-scale problems.  IEEE Transactions on Neural Networks and Learning Systems, 27(4):783-795, 2016, DOI: 
10.1109/TNNLS.2015.2424684(pdf, code)
42. Manfred K.Warmuth, Wojciech Kotłowskib, Shuisheng Zhou. 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.
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.

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. in 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.

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 Algorithm 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.
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 the principal components. 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.
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)
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)
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)
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)
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.
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.
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)
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.
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)
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.
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.
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.
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.
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.
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.