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
-
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
-
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
-
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
-
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
-
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)
-
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)
-
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)
-
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)
-
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)
-
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
-
Yun-Long Feng and Shao-Gao Lv.
(2011). Unified approach to coefficient-based regularized regression. Computer
and Mathematic With Application. 62, 506--515. (SCI)
-
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)
-
Shaogao Lv* and Fanyin Zhou. (2015) Optimal
learning rates of L^p-type multiple kernel learning under general conditions.
Information Science, (10) 255–268
-
Shaogao
Lv*. (2015). Refined generalization
bounds of gradient learning over reproducing Kernel Hilbert spaces . Neural
Computation, (27), 1294–1320