王 震

2016-11-29 文字:  点击:[]

姓名:王震

职称:讲师

部门:信息与计算科学系

研究方向:模式识别

邮箱:wangzhen@imu.edu.cn

最优化与人工智能研究小组成员(http://www.optimal-group.org/)

个人主页

http://www.optimal-group.org/member/wz.html

个人简介

2002-2006吉林大学本科

2008-2010吉林大学硕士

2011-2014吉林大学博士

主持项目

2012-2013吉林大学研究生创新基金(20121053)

2014-2016内蒙古大学科研启动项目

2015-2017内蒙古自然科学基金博士基金(2015BS0606)

2016-2018国家自然科学基金青年基金(11501310)

发表论文

[1] Zhen Wang, Y.H. Shao, L. Bai, Chun-Na Li, Li-Ming Liu, N.Y. Deng. MBLDA: A novel multiple between-class linear discriminant analysis. Information Sciences, 2016, 369: 199-220.

[2] Zhen Wang, Y.H. Shao, L. Bai, N.Y. Deng. Twin Support Vector Machine for Clustering. IEEE Transactions on Neural Networks and Learning Systems, 2015, DOI: 10.1109/TNNLS.2014.2379930.

[3] Zhen Wang, Y.H. Shao, T.R. Wu. Proximal parametric-margin support vector classifier and its applications. Neural Computing and Applications, 2014, 24 (3-4), 755-764.

[4] Zhen Wang, Y.H. Shao, T.R. Wu. A GA-based Model Selection for Smooth Twin Parametric-Margin Support Vector Machine. Pattern Recognition,2013, 46: 22672277.

[5] Zhen Wang, J. Chen, M. Qin, Non-parallel Planes Support Vector Machine for Multi-class Classification. Logistics Systems and Intelligent Management, Vol. 1, pp. 581-585, 2010.

[6] Zhen Wang, D.M. Li, Multiple-Instance Classification via Generalized Eigenvalue Proximal SVM. Advanced Materials Research, Vol. 143, pp. 1235-1239, 2010.

[C1] Y.H. Shao, W.J. Chen, Zhen Wang, Chun-Na Li, Nai-Yang Deng. Weighted linear loss twin support vector machine for large-scale classification.Knowledge-Based Systems, 73: 276-288 (2015).

[C2] Y.H. Shao, C.N. Li, Zhen Wang, M.Z. Liu, N.Y. Deng. Proximal Classifier via Absolute Value Inequalities. In: Proceedings of the 14th IEEE International Conference on Data Mining Workshops (ICDM'14), Shenzhen, China, 2014.

[C3] Y.H. Shao, W.J. Chen, Zhen Wang, H.B. Zhang, N.Y. Deng. A proximal classifier with positive and negative local regions. Neurocomputing, 2014, 145:131-139.

[C4] Y.H. Shao, Zhen Wang, Z.M. Yang, N.Y. Deng. Weighted linear loss support vector machine for large scale problems. Procedia Computer Science (IAITQM), 2014,31C: 639-647.

[C5] Y.H. Shao, W.J. Chen, J.J. Zhang, Zhen Wang, N.Y. Deng. An efficient weighted Lagrangian twin support vector machine for imbalanced data classification. Pattern Recognition, 2014,47(9): 3158-3167.

[C6] L. Bai, Zhen Wang, Y.H. Shao, N.Y. Deng, A novel feature selection method for twin support vector machine. Knowledge-Based Systems, Volume 59, 1-8, 2014.

[C7] Y.H. Shao, L. Bai, Zhen Wang, X.Y. Hua, N.Y. Deng. Proximal Plane Clustering via Eigenvalues. Procedia Computer Science(IAITQM), 2013,17: 4147.

[C8] Y.F. Ye, H. Cao, L. Bai, Zhen Wang, Y.H. Shao. Exploring Determinants of Inflation in China Based on L1-Epsilon-Twin Support Vector Regression.Procedia Computer Science (IAITQM), 2013,17:514522.

[C9] Y.H. Shao, Zhen Wang, W.J. Chen, N.Y. Deng. Least squares twin parametric-margin support vector machines for classification. Applied Intelligence, 2013,39 (3), 451-464.

[C10] Y.H. Shao, N.Y. Deng, W.J. Chen, Zhen Wang. Improved generalized eigenvalue proximal support vector machine. IEEE Signal Processing Letters, 2013, 20(3):213- 216.

[C11] Y.H. Shao, Zhen Wang, W.J. Chen, N.Y. Deng. A regularization for the projection twin support vector machine. Knowledge-Based Systems, 2013,37:203210.

[C12] Y.H. Shao, N.Y. Deng, Z.M. Yang, W.J. Chen, Zhen Wang. Probabilistic outputs for twin support vector machines. Knowledge-Based Systems, 2012, 33: 145151.

 

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