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  Correspondence

  • Name:   Nai-Yang Deng
  • E-mail:   dengnaiyang@cau.edu.cn
  • Phone:   +86-10-62736265(H)
  • Address:   College of Science, China Agriculture Univercity, P.O.Box 483, Beijing 100083, China
  • Nai-Yang Deng received his B.Sc. and M.Sc.degrees in the Department of Mathematics and Mechanics from Peking University, China, in 1962 and 1966, respectively. He joined the Department of Science College of China Agriculture University as a professor in 1990, he is a Part-time Professor of Shanghai University from 1994. He has wide research interests, mainly including computational methods for optimization, operation research, support vector machine in data mining and bioinformatics. In these areas, he has published over 100 papers in leading international journals or conferences.
  • Last Modified: 2015-01-10

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      Research Interests

  • Optimization Theory and Application

         Convex optimization

         Mathematical analysis of SVM

         Global Convergence Algorithms

         Linear Programming and Combinatorics

  • ML/DM topics

         Supervised and unsupervised learning

         Semi-supervised and active learning

         Multi-instance and multi-label learning

         Dimensionality reduction and feature selection

         Systems biology and bioinformatics

         Image process and texture recognition

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      Recent Publications


    {Recent Books} {2015} {2014} {2013} {2012} {2011}
    * means the corresponding author.
    Recent Books
  • N.-Y. Deng, Y.-J. Tian, C.-H. Zhang. Support Vector Machines: Optimization-Based Theory, Algorithms, and Extensions. CRC Press, 2013.

    2015
  • Yuan-Hai Shao, Wei-Jie 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].

    2014
  • Zhen Wang , Yuan-Hai Shao*, Lan Bai, Nai-Yang Deng. Twin Support Vector Machine for Clustering. IEEE Transactions on Neural Networks and Learning Systems, 2014, DOI: 10.1109/TNNLS.2014.2379930. [Code].
  • Chun-Na Li, Yuan-Hai Shao*, Nai-Yang Deng. Robust L1-norm nonparallel proximal support vector machine. Optimization, 2014, DOI: 10.1080/02331934.2014.994627. [Code].
  • Yuan-Hai Shao, Nai-Yang Deng*. The Equivalence between Principal Component Analysis and Nearest Flat in the Least Square Sense. Journal of Optimization Theory and Applications, 2014, DOI: 10.1007/s10957-014-0647-y.
  • Yuan-Hai Shao*, Chun-Na Li, Zhen Wang , Ming-Zeng Liu, Nai-Yang Deng. Proximal Classifier via Absolute Value Inequalities. In: Proceedings of the 14th IEEE International Conference on Data Mining Workshops (ICDM'14), Shenzhen, China, 2014.
  • Yuan-Hai Shao, Wei-Jie Chen,Zhen Wang, Hai-Bin Zhang, Nai-Yang Deng*. A proximal classifier with positive and negative local regions. Neurocomputing, 2014, 145:131-139.
  • Yuan-Hai Shao, Zhen Wang, Zhi-Min Yang*, Nai-Yang Deng*. Weighted linear loss support vector machine for large scale problems. Procedia Computer Science(IAITQM), 2014,31C: 639-647.
  • Wei-Jie Chen*, Yuan-Hai Shao*, Nai-Yang Deng, Zhi-Lin Feng. Laplacian least squares twin support vector machine for semi-supervised classification. Neurocomputing, 2014,145:465-476.
  • Yuan-Hai Shao, Wei-Jie Chen, Jing-jing Zhang, Zhen Wang, Nai-Yang Deng*. An efficient weighted Lagrangian twin support vector machine for imbalanced data classification. Pattern Recognition, 2014, 47(9): 3158-3167.[Code].
  • Lan Bai, Zhen Wang, Yuan-Hai Shao*, Nai-Yang Deng. A novel feature selection method for twin support vector machine. Knowledge-Based Systems, 2014, 59 1-8.[Code].
  • Yuan-Hai Shao, Wei-Jie Chen,Nai-Yang Deng*. Nonparallel hyperplane support vector machine for binary classification problems. Information Sciences, 2014, 263(1) 2014, 22–35.[Code].
  • Zhang, Z., Zhen, L., Deng, N., & Tan, J. Sparse least square twin support vector machine with adaptive norm. Applied Intelligence, 2014, 41(4), 1097-1107.
  • Zhang, Z., Ke, T.,N.-Y. Deng, Tan, J. Biased p-norm support vector machine for PU learning. Neurocomputing, 2014, 136, 256-261.
  • Xu, Y., Wang, X., Wang, Y., Tian, Y., Shao, X., Wu, L. Y.,N.-Y. Deng*. Prediction of posttranslational modification sites from amino acid sequences with kernel methods. Journal of theoretical biology, 344 (2014) 78–87.
  • Tan, J., Zhen, L., Deng, N., & Zhang, Z. Laplacian p-norm proximal support vector machine for semi-supervised classification. Neurocomputing, 2014, 144, 151-158.
  • Xu, Y., Wen, X., Wen, L. S., Wu, L. Y., Deng, N. Y., & Chou, K. C. iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PloS one, 2014, 9(8), e105018.
  • Xu, Y., Wen, X., Shao, X. J., Deng, N. Y., & Chou, K. C. iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. International journal of molecular sciences, 2014, 15(5), 7594-7610.

    2013
  • Y.-H. Shao, N.-Y. Deng*, W.-J. Chen. A proximal classifier with consistency. Knowledge-Based Systems,2013, 49:171-178 [Code].
  • Y.-H. Shao, N.-Y. Deng, W.-J. Chen, Z. Wang. Improved generalized eigenvalue proximal support vector machine. IEEE Signal Processing Letters, 2013, 20(3):213- 216.
  • Xu, Y., Shao, X. J., Wu, L. Y., N.-Y. Deng, Chou, K. C.. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ,2013, 1, e171.
  • Yong-Cui Wang, Shi-Long Chen, N.-Y. Deng, Yong Wang: Network predicting drug's anatomical therapeutic chemical code. Bioinformatics, 29(10): 1317-1324 (2013)
  • Y.-H. Shao, L. Bai, Z. Wang, X.-Y. Hua, N.-Y. Deng*. Proximal Plane Clustering via Eigenvalues. Procedia Computer Science (IAITQM), 2013,17: 41–47.
  • Y.-H. Shao, W.-J. Chen, W.-B. Huang, Z.-M. Yang, N.-Y. Deng*. The Best Separating Decision Tree Twin Support Vector Machine for Multi-Class Classification. Procedia Computer Science (IAITQM), 2013,17: 1032-1038.
  • Y.-H. Shao, C.-H. Zhang, Z.-M. Yang, L. Jing, N.-Y. Deng*. An \varepsilon-twin support vector machine for regression. Neural Computing and Applications,2013, 23:175–185 [Code].
  • Y.-H. Shao, N.-Y. Deng*. A novel margin based twin support vector machine with unity norm hyperplanes. Neural Computing and Applications, 2013, 22(7-8):1627-1635.
  • Y.-H. Shao, Z. Wang, W.-J. Chen,N.-Y. Deng*. Least squares twin parametric-margin support vector machines for classification. Applied Intelligence, 2013,39(3):451-464.
  • Y.-H. Shao,Z. Wang, W.-J. Chen, N.-Y. Deng*. A regularization for the projection twin support vector machine. Knowledge-Based Systems, 2013,37:203–210.

    2012
  • Y.-H. Shao,N.-Y. Deng*, Z.-M. Yang, W.-J. Chen, Z. Wang. Probabilistic outputs for twin support vector machines. Knowledge-Based Systems, 2012, 33: 145–151. [Code].
  • Y.-H. Shao,N.-Y. Deng*.A coordinate descent margin based-twin support vector machine for classification. Neural Networks, 2012, 25: 114–121.
  • Y.-H. Shao, N.-Y. Deng*, Z.-M. Yang.Least squares recursive projection twin support vector machine for classification. Pattern Recognition, 2012, 45(6): 2299-2307. [Code].
  • C.-H. Zhang, Y.-H. Shao, J.-Y. Tan, N.-Y. Deng. Mixed-norm Linear Support Vector Machine. Neural Computing and Applications, 2012, DOI: 10.1007/s00521-012-1166-0

    2011
  • Y.-H. Shao, C.-H. Chun, X.-B. Wang, N.-Y. Deng*.Improvements on Twin Support Vector Machines. IEEE Transactions on Neural Networks, vol.22 no.6 pp. 962-968, 2011. [Code] [Data].
  • Y.-X. Li, Y.-H. Shao, L. Jing, N.-Y. Deng*. An Efficient Support Vector Machine Approach for Identifying Protein S-Nitrosylation Sites. Protein \& Peptide Letters, 2011, 18(6): 573-587(15).
  • Y.-X. Li, Y.-H. Shao,N.-Y. Deng*. Improved Prediction of Palmitoylation SitesUsing PWMs and SVM. Protein \& Peptide Letters,2011, 18(2): 186-193(8).

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