报告人：邹斌（湖北大学）

报告题目：Support Vector Machine Classification Based on Markov Sampling 

报告人简介：现为湖北大学教授、博士生导师，研究方向为统计学习理论、机器学习，在“IEEE Transactions on Neural Networks and Learning System”，“ IEEE Transactions on Cybernetics”，“Neural Networks”，“Journal of Computer and Mathematics with Applications”等国际重要期刊发表学术论文20多篇.

报告摘要：Support Vector Machine (SVM) is one of the most widely used learning algorithms for classification problems. Although SVM has good performance in practical applications, it has high algorithmic complexity as the size of training samples is large. In this paper we introduce SVM classification (SVMC) algorithm based on k-times Markov sampling and present the numerical studies on the learning performance of SVMC with k-times Markov sampling for benchmark datasets. The experimental results show that the SVMC algorithm with k-times Markov sampling  not only have smaller misclassification rates, less time of sampling and training, but also the obtained classifier is more sparse compared to the classical SVMC and the previously known SVMC algorithm based on Markov sampling. We also give some discussions on the performance of SVMC with k-times Markov sampling.

报告时间：2016年11月7日 晚上18:30

报告地点：科技楼南楼602室