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吕绍高
  • 吕绍高
  • 系别:西南财经大学统计研究中心
  • 职称:副教授
  • 办公电话:28
  • Email:lvsg716@swufe.edu.cn
  • 教师简介
  • 研究成果
  • 学术活动
  • 教授课程
  • 学术活动:

    2011年5月, 第四届调和分析国际会议(香港),报告题目:Integral operator approach to learning theory with unbounded sampling;

    2012年5月, 第二届计算调和分析研讨会(上海),报告题目:Fast learning rates of coefficient based regularization with indefinite kernel in a general framework;

    2013年5月,逼近论及其应用国际学术会议(香港),报告题目:Optimal learning rates for multiple kernel learning within a general framework;

    2013年9月,学习理论研讨会(绍兴),报告题目:Semi-parametric efficient estimate for generalized additive models。

  • 研究成果加主持项目:

    1、国家自然科学基金青年项目:高维数据框架内的非参与半参分位数回归模型的研究,No.11301421, (2014-2016)

    2、中央高校专项资金-交叉创新项目:不依赖于模型的可大规模计算的变量选择方法. No.JBK140210.(2014-2015).

    3、中央高校基本科研业务费专项资金--交叉创新项目:有关稀疏型的非参分位数回归模型的研究,No.JBK130219, (2013-2014)。

    4、 国家自然科学基金天元专项基金:基于凸正则化项的多核学习算法的理论研究,No.11226111,(2012-2013)。

    5、 中央高校基本科研业务费专项资金--年度培育项目:一类多核学习算法的统计特性研究,No. JBK120940,(2012-2013)。

    6、 西南财经大学“211工程”三期青年教师成长项目:非正定核学习算法的理论基础与应用研究,No. 211QN2011028, (2011-2012)。

    Accepted or published papers :  

    18.S. G. Lv, H. Z. Lin, H.Lian and J. Huang,Oracle Inequalities for Sparse Additive Quantile Regression in Reproducing Kernel Hilbert Space. (2017) Annals of Statistics. Accepted

    17. X. He, J. H. Wang and S.G. Lv(通信作者),Gradient-induced Model-free Variable Selection with Composite Quantile Regression. (2017), Statistica Sinica accepted

    16.Shaogao Lv, Xin He, Junhui Wang(2016) A unified penalized method for sparse additive quantile models: an RKHS approach,Ann Inst Stat Math,DOI 10.1007/s10463-016-0566-9

    15. Guo Niu, Zhengming Ma, Shaogao Lv(2016)Ensemble Multiple-Kernel Based Manifold Regularization,Neural Process Lett,DOI 10.1007/s11063-016-9543-9  

    14. Shaogao Lv and Luhong Wang (2016) Linearity Identification for General Partial Linear Single-Index Models ,Mathematical Problems in Engineering,Volume 2016, Article ID 3537564, 7 pages

    13.Lei Yang, Shaogao Lv and Junhui Wang*. (2016). Model-free Variable Selection in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research.,17 ,1-24.
    12.Yunlong Feng, Shaogao Lv*, Hanyuan Han, Johan A.K. Suykens. (2016). Kernelized Elastic Net Regularization: Generalization Bounds and Sparse Recovery. Neural Computation, 28, 525–562 .

    11.ShaogaoLv*. (2015). Refined generalization bounds of gradient learning overreproducing Kernel Hilbert spaces . Neural Computation, (27), 1294–1320.

    10.ShaogaoLv* and Fanyin Zhou. (2015). Optimal learning rates of L^p-type multiple kernellearning under general conditions. Information Science, (10) 255-268.

    9.Yue Liu,Bingjie Wang and Shaogao Lv.(2014) Using multi-class adaboost tree for prediction frequency of auto insurance. Journal of Applied Finance & Banking, (4), 45-53.

    8.Shao-Gao Lv, Dai-Min Shi, Quan Wu Xiao and Ming Shan Zhang.(2013) Sharp learning rates of coefficient-based l^p-regularized regression with indefinite kernels.Science China Mathematics,56(8), 1557-1574 (SCI).

    7.Shao-Gao Lv, Tie-Feng Ma, Liu Liu and Yun-Long Feng. (2013) . Fast learning rates for sparse quantile regression problem. Neurocomputing, 108, 13-22 (SCI)

    6. Shao-Gao Lv and Yun-Long Feng. (2013). Consistency of coefficient-based spectral clustering with l^1-regularizer. Mathematics and Computer Modeling. 57, 469--482 (SCI)

    5. ShaoGao Lv and YunLong Feng. (2012). Integral operator approaches to learning theory with unbounded sampling. Complex Analysis and Operator Theory. 6, 533--548.(SCI)

    4. Shao-Gao Lv and Yun-Long Feng. (2012). Semi-supervised learning with the help of Parzen windows. Journal of Mathematics Analysis and Applications. 386, 205--212. (SCI)

    3.ShaoGao Lv and Jinde Zhu. (2012). Error bounds for lp-norm multiple kernel learning with least square loss. Abstract and Applied Analysis, Article ID 915920, 18 pages.doi:10.1155/2012/915920.

    2.Yun-Long Feng and Shao-Gao Lv. (2011). Unified approach to coefficientbased regularized regression. Computer and Mathematic With Application. 62, 506-515. (SCI)

    1.ShaoGao Lv and Lei Shi. (2010). Learning theory viewpoint of approximation by positive linear operators. Computer and Mathematics with Application. 60, 3177--3186. (SCI) 

  • 学术活动:

    2011年5月, 第四届调和分析国际会议(香港),报告题目:Integral operator approach to learning theory with unbounded sampling;

    2012年5月, 第二届计算调和分析研讨会(上海),报告题目:Fast learning rates of coefficient based regularization with indefinite kernel in a general framework;

    2013年5月,逼近论及其应用国际学术会议(香港),报告题目:Optimal learning rates for multiple kernel learning within a general framework;

    2013年9月,学习理论研讨会(绍兴),报告题目:Semi-parametric efficient estimate for generalized additive models。

  • 讲授课程:

    本 科:财经数据挖掘、随机过程

    研究生:财经数据挖掘、随机过程、高等数理统计