基本情况

吕绍高，西南财经大学统计学院副教授，博士生导师。


研究领域

Statistical Learning and Data Mining. High Dimensional Data Analysis.Nonparametric Estimation. Machine Learning for Financial Engineering


教育背景

2006年9月至2011年7月 联合培养博士，中国科技大学与香港城市大学

2002年9月至2006年7月 学士，河南师范大学数学与信息科学学院


主持课题

  1. 国家自然科学基金天元数学基金：基于凸正则化项的多核学习算法的理论研究，No11226111，（2012），进行中
  2. 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，No211QN2011028，（2012），已结项
  3. 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，NoJBK120940，（2012），已结项
  4. 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）


论文发表

  1. Shao-Gao Lv, Dai-Min Shi, Quan Wu Xiao and Ming Shan Zhang.Sharp learning rates of coefficient-based l^p-regularized regression with indefinite kernel. Science China Mathematics, 56(8), 1557-1574, Accepted. (SCI)
  2. Shao-Gao Lv, Tie-Feng Ma, Liu Liu and Yun-Long Feng. (2013). Fast learning rates for sparse quantile regression problem. Neurocomputing, Accepted, DOI: 10.1016/j.neucom.2012.10.015. (SCI)
  3. 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)
  4. Shao-Gao Lv and Yun-Long Feng. (2012). Integral operator approaches to learning theory with unbounded sampling. Complex Analysis and Operator Theory. 6, 533--548.(SCI)
  5. 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)
  6. Shao-Gao Lv and Jinde Zhu. Error bounds for lp-norm multiple kernel learning with least square loss. Abstract and Applied Analysis, Volume 2012, Article ID 915920, 18 pages.doi:10.1155/2012/915920
  7. Yun-Long Feng and Shao-Gao Lv. (2011). Unified approach to coefficient-based regularized regression. Computer and Mathematic With Application. 62, 506--515. (SCI)
  8. Shao-Gao Lv and Lei Shi. (2010). Learning theory viewpoint of approximation by positive linear operators. Computer and Mathematics with Application. 60, 3177--3186. (SCI)
  9. Shaogao Lv* and Fanyin Zhou. (2015) Optimal learning rates of L^p-type multiple kernel learning under general conditions. Information Science, (10) 255–268
  10. Shaogao Lv*.  (2015). Refined generalization bounds of gradient learning over reproducing Kernel Hilbert spaces . Neural Computation, (27), 1294–1320