报告人：邹斌 (湖北大学)

报告题目：New Incremental Learning Algorithm with Support Vector Machines

报告人简介：邹斌，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。主持省部级、国家级科学基金共计7项，在国际国内知名期刊上发表论文40余篇。

报告摘要: Incremental learning is one of the effective methods of learning the accumulated data and the large-scale data. The newly-increased samples of the previously known works on incremental learning are usually independent and identically distributed (i.i.d.). To study how dependent sampling method influence on the learning ability of incremental support vector machines (ISVM) algorithm, in this paper we introduce an ISVM based on Markov resampling (MR-ISVM), and give the experimental researches on the learning ability of MR-ISVM algorithm.The experimental results indicate that MR-ISVM algorithm has not only smaller misclassification rates and sparser of the obtained classifiers, but also less total time of sampling and training  compared to ISVM based on randomly independent sampling (RIS-ISVM). We also compare it with other incremental SVM algorithms.

报告时间: 2018年5月16日（星期三）上午10：30-11：30

报告地点: 科技楼南楼602室