报告人：陈洪 (华中农业大学)

报告题目：Group Sparse Additive Machine

报告人简介：陈洪，教授，博士生导师，湖北省优秀博士论文获得者。2009年6月在湖北大学获基础数学博士学位。2016.2-2017.8在University of Texas at Arlington 从事博士后研究，多次受邀赴澳门大学、香港城市大学从事合作研究。主持国家自然科学基金面上项目1项、青年基金1项，主持中央高校创新团队培育项目1项、优秀人才培育项目1项。在国际期刊发表论文30余篇，在机器学习顶级会议NIPS发表论文3篇。

报告摘要:A family of learning algorithms generated from additive models have attracted much attention recently for their flexibility and interpretability in high dimensional data analysis. In this talk, we introduce a new classification method, called as group sparse additive machine (GroupSAM), to explore and utilize the structure information among the input variables. Generalization error bound is established by integrating the sample error analysis with empirical covering numbers and the hypothesis error estimate with the stepping stone technique. Our new bound shows that GroupSAM can achieve a satisfactory learning rate with polynomial decay. Experimental results on synthetic data and seven benchmark datasets show the effectiveness of our approach.

报告时间: 2018年5月16日（星期三）下午15：00-16：00

报告地点: 科技楼南楼602室