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Correspondence Biography Interests Publications

Correspondence

  • Name:   Zhen Wang
  • E-mail:   wangz11@mails.jlu.edu.cn   wangzhen1882@126.com
  • Phone:   (+86)0471-4991930
  • Address:   School of Mathematical Sciences, Inner Monggolia University, Hohhot, 010021, China

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    Biography

    Zhen Wang received his Doctor’s degree in College of Mathematics from Jilin University, China, in 2014. Currently, he is lecturer in School of Mathematical Sciences from Inner Monggolia University. His research interests include pattern recognition, text categorization, and data mining.


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    Research Interests

  • Optimization Theory and Application

         Linear Programming and Combinatorics

         Global Convergence Algorithms

         Mathematical analysis of SVM

         SVM solving optimization problems

  • ML/DM topics

         Supervised learning

         Multi-instance learning

         Unsupervised and active learning

         Dimensionality reduction and feature selection

  • Applications

         Text classification

         Image recognition and face recognition

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    Publications

    Total SCI index First author Co-author
    18 12 6 12

  • 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. [Code].
  • 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)[Code].
  • 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. [Code].
  • 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.
  • 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.
  • 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.
  • Y.H. Shao, W.J. Chen, J.J. Zhange Zhen Wang, N.Y. Deng. An efficient weighted Lagrangian twin support vector machine for imbalanced data classification. Pattern Recognition, 2014,47(9): 3158-3167.
  • 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
  • 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.
  • Y.H. Shao, L. Bai, Zhen Wang, X.Y. Hua, N.Y. Deng. Proximal Plane Clustering via Eigenvalues. Procedia Computer Science(IAITQM), 2013,17: 41–47.
  • 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), 2013,17:514–522.
  • 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
  • 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: 2267–2277. [Code].
  • 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.
  • Y.H. Shao, Zhen Wang, W.J. Chen, N.Y. Deng. A regularization for the projection twin support vector machine. Knowledge-Based Systems, 2013,37:203–210.
  • 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: 145–151.[Code].
  • 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.
  • Zhen Wang, D.M. Li, Multiple-Instance Classification via Generalized Eigenvalue Proximal SVM. Advanced Materials Research, Vol. 143, pp. 1235-1239, 2010.

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