Songfeng's Publication page


My research focuses on applied and computational statistics, specifically, data mining, efficient quantile regression algorithms, high dimensional data analysis, reliable statistical machine learning models and the corresponding high-performance optimization algorithms, applications to real-world problems, such as medical and natural image analysis, bioinformatics.

Peer Reviewed Journal Articles:

  1. S. Zheng, “A Majorization-Minimization Scheme for Support Vector Regression”, (in writing) 

  2. S. Zheng, “KLERC: Kernel Lagrangian Expectile Regression Calculator”, Computational Statistics, (under review) 

  3. S. Zheng, “A Newton Algorithm for Support Vector Machine and Support Vector Data Description”, Pattern Recognition, (under review)

  4. S. Zheng, “Iteratively Reweighted Least Square for Asymmetric L2-Loss Support Vector Regression”, Communications in Statistics - Simulation and Computation, (Accepted)

  5. S. Zheng, “A Fast Iterative Algorithm for Support Vector Data Description”, International Journal of Machine Learning and Cybernetics, Vol. 10, Issue 5, pp. 1173-1187, 2019.

  6. S. Zheng, “An improved Bennett's inequality”, Communications in Statistics - Theory and Methods, Vol. 47, no. 17, pp. 4152-4159, 2018.

  7. S. Zheng, “A refined Hoeffding’s upper tail probability bound for sum of independent random variables”, Statistics & Probability Letters, Vol. 131, pp. 87-92, 2017.

  8. X. Miao, H. Xie, S. F. Ackley, S. Zheng, "Object-Based Arctic Sea Ice Ridge Detection From High Spatial Resolution Imagery", IEEE Geoscience and Remote Sensing Letters, Vol. 13, no. 6, pp. 787-791, 2016.

  9. S. Zheng, “Smoothly Approximated Support Vector Domain Description”, Pattern Recognition, Vol. 49, No. 1, pp. 55-64, 2016. 

  10. S. Zheng, “A Fast Algorithm for Training Support Vector Regression via Smoothed Primal Function Minimization”, International Journal of Machine Learning and Cybernetics, Vol. 6, No. 1, pp. 155 - 166, 2015.

  11. S. Zheng, “A Generalized Newton Algorithm for Quantile Regression Models”, Computational Statistics, Vol. 29, Issue 6, pp. 1403-1426, 2014

  12. S. Zheng and W. Liu, “Functional Gradient Ascent for Probit Regression”, Pattern Recognition,  Vol. 45, No. 12, pp. 4428-4437, 2012.

  13. X. Miao, J. S. Heaton, S. Zheng, D. A. Charlet, and H. Liu, “Applying Tree-based Ensemble Algorithms to Ecosystem Classification Using Multi-temporal Multi-source Remote Sensing Data”, International Journal of Remote Sensing, Vol. 33, No. 6, pp. 1823-1849, 2012.

  14. S. Zheng and W. Liu, “An Experimental Comparison of Gene Selection by Lasso and Dantzig Selector for Cancer Classification”, Journal of Computers in Biology and Medicine, vol. 41, no. 11, pp. 1033 – 1040, 2011.

  15. S. Zheng, “QBoost: Predicting Quantiles with Boosting for Regression and Binary Classification”, Journal of Expert Systems With Applications, Vol. 39, Issue 2, pp. 1687-1697.

  16. S. Zheng, “Gradient Descent Algorithms for Quantile Regression with Smooth Approximation”, International Journal of Machine Learning and Cybernetics, Vol. 2, Issue 3, pp. 191-207, 2011.

  17. Q. Zou, S. Zheng, and A. H. Sayed, “Cooperative Spectrum Sensing via Sequential Detection for Cognitive Radio Networks”, IEEE Transactions on Signal Processing, Vol. 58, no. 12, pp. 6266 – 6283, 2010.

  18. S. Zheng, A. Yuille, and Z. Tu, “Detecting Object Boundaries Using Low-, Mid-, and High-Level Information”, Journal of Computer Vision and Image Understanding, Vol. 114, no. 10, pp. 1055-1067, 2010.

  19. L. He, S. Zheng, and L. Wang, “Integrating Local Distribution Information with Level Set for Boundary Extraction”, Journal of Visual Communication and Image Representation, vol. 21, pp. 343-354, 2010.

  20. Z. Tu, S. Zheng, and A. Yuille, “Shape Matching and Registration by Data-driven EM”, Journal of Computer Vision and Image Understanding, Vol. 109, No. 3, pp. 290-304, 2008.

  21. Z. Tu, S. Zheng, A. Yuille, A. Reiss, R. Dutton, A. Lee, A. Galaburda, I. Dinov, P. Thompson, and A. Toga, “Automated Extraction of the Cortical Sulci based on a Supervised Learning Approach”, IEEE Transactions on Medical Imaging, Vol. 26, No. 4, pp. 541-552, 2007.

  22. W. Liu, N. Zheng, and S. Zheng, “Learning Sparse Mixture Models for Discriminative Classification”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 20, No. 3, pp. 431-440, 2006.

  23. X. Lu, N. Zheng, and S. Zheng, “Support Vector Machines for Information Retrieval”, Journal of Xi'an Jiaotong University, vol.37, No.6, pp. 581-585, 2003. (in Chinese).

Technical Notes:

  1. S. Zheng, “A Generalized Alternating Harmonic Series”, 2015.

  2. S. Zheng, “An Improved Hoeffding's Inequality”, 2016.

Peer Reviewed Conference Articles:

Note: In the areas of image analysis and machine learning, conference publication is also very important. The conferences on which I published have double-blind review process, that is, reviewers and authors don’t know each other. The acceptance rates are usually below 30%, which are considered as competitive in these areas.

  1. S. Zheng, “Labeling Image Patches by Boosting based Median Classifier”, British Machine Vision Conference (BMVC), Dundee, UK, Sept. 2011.

  2. S. Zheng, “Boosting based Conditional Quantile Estimation for Regression and Binary Classification”, the 9th Mexican International Conference on Artificial Intelligence (MICAI), Pachuca, Mexico, Nov. 2010.

  3. S. Zheng, “Probabilistic Cascade Random Fields for Man-made Structure Detection”, the 9th Asian Conference on Computer Vision (ACCV), Xi'an, China, Sept. 2009.

  4. A. Yuille and S. Zheng, “Compositional Noisy-Logical Learning”, the 26th International Conference on Machine Learning (ICML), Montreal, Canada, June 2009.

  5. J. Jiang, S. Zheng, A. Toga, and Z. Tu, “Learning based Coarse-to-fine Image Registration”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, June 2008.

  6. S. Zheng, Z. Tu, and A. Yuille, “Detecting Object Boundaries Using Low-, Mid-, and High-level Information”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN, June 2007.

  7. S. Zheng, Z. Tu, A. Yuille, A. Reiss, R. Dutton, A. Lee, A. Galaburda, I. Dinov, P. Thompson, and A. Toga, “A Learning-based Algorithm for Automated Extraction of the Cortical Sulci”, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Copenhagen, Denmark, Oct. 2006.

  8. S. Zheng, X. Lu, N. Zheng and W. Xu, “Unsupervised Clustering based Reduced Support Vector Machines”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hong Kong, China, 2003.

  9. S. Zheng, N. Zheng and F. Sun, “Threshold Image Segmentation based on Fuzzy Set Theory and Promoted Genetic Algorithm”, International Symposium on Information Theory and its Application (ISITA), Xi’an, China, 2002. (Young Researcher Award).