报告人：邹斌 教授（湖北大学）

报告题目：Kernelized Elastic Net Regularization Based on Markov Selective Sampling

报告摘要：In this talk, we extend Kernelized Elastic Net Regularization (KENReg) algorithm from the assumption of independent and identically distributed (i.i.d.) samples to the case of non-i.i.d. samples. We first establish the generalization bounds of KENReg algorithm with uniformly ergodic Markov chain samples, then we prove that the KENReg algorithm with uniformly ergodic Markov chain samples is consistent and obtain the fast learning rate of KENReg algorithm with uniformly ergodic Markov chain samples. We also introduce the KENReg algorithm based on Markov selective sampling. Based on Gaussian kernels, the advantages of KENReg algorithm against the traditional one with i.i.d. samples are demonstrated on various real-world data sets. Compared to randomly independent sampling, experimental results show that the KENReg algorithm based on Markov selective sampling not only has much higher prediction accuracy in terms of mean square errors and generates simpler models in terms of the number of non-zero regression coefficients, but also has shorter total time of sampling and training. We compare the algorithm proposed in this paper with these known regularization algorithms, like kernelized Ridge regression and kernelized least absolute shrinkage and selection operator (Lasso).

报告人简介：邹斌，博士，湖北大学数学与统计学学院教授、博士生导师。2007年6月博士毕业于湖北大学基础数学专业，博士学位论文获2008年湖北省优秀博士学位论文。2008年1月至2009年12月在西安交通大学信息与系统科学研究所进行博士后研究工作，合作导师为徐宗本教授。当前主要研究兴趣为统计学习理论，机器学习，大数据分析等。主持省部级、国家级科学基金共计7项，以第一作者或通讯作者在《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Transactions on Cybernetics》、《Machine Learning》、《Neural Networks》、《中国科学》等国际国内知名期刊上发表论文40余篇。

报告时间：2018年10月9日（星期二）上午9:30-10:30

报告地点：科技楼南楼602室