報告人：鄒斌（湖北大學）

報告題目：Support Vector Machine Classification Based on Markov Sampling 

報告人簡介：現為湖北大學教授、博士生導師，研究方向為統計學習理論、機器學習，在“IEEE Transactions on Neural Networks and Learning System”，“ IEEE Transactions on Cybernetics”，“Neural Networks”，“Journal of Computer and Mathematics with Applications”等國際重要期刊發表學術論文20多篇.

報告摘要：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.

報告時間：2016年11月7日 晚上18:30

報告地點：科技樓南樓602室