报告题目：Large data classification via support vector machines

报 告 人：Professor Xiaoou Li, National Autonomous University of Mexico

报告时间：2016年3月16日（星期三）9：30 - 10：30

报告地点：信息学馆301会议室

邀请单位：电气自动化研究所

Abstract:

Support Vector Machines (SVM) has demonstrated highly competitive performance in many real-world applications. However, despite its good theoretical foundations and generalization performance, SVM is not suitable for classifying large data sets because of high training complexity. In recent years, we have introduced several data reduction techniques into SVM classification process to handle this problem, such as minimum enclosure ball, clustering, convex-concave hull, decision tree, etc.. Experiments showed that SVM is still suitable for large data classification if the training data is sufficient representative. In this talk, I will present these data selection or reduction techniques, as well as the classification process. 

Biography:

Dr. Xiaoou Li obtained B. S degree of applied mathematics in 1991 and PhD degree of Automatic Control in 1995 from Northeastern University, Shenyang, P. R. China. She had worked in Northeastern University, China for two years after PhD study; then she was a postdoc of National Autonomous University of Mexico (UNAM) from 1998 to 2000. Since April, 2000, she has been a professor of Department of Computer Science, The Research and Advanced Studies Centre of the National Polytechnic Institute (CINVESTAV-IPN), Mexico. She was a senior research fellow of School of Electronics, Electrical Engineering & Computer Science, Queen's University Belfast, UK during the school year 2006-2007 (sabbatical leave); and school of Engineering, University of California Santa Cruz in 2010 (sabbatical leave). Currently she is a senior member of IEEE, member of AMC (Mexican Association of Science), and member of SNI (National Researcher System) level 2.

Dr. Li has published more than 100 papers on international journals, book chapters and conferences. She has successfully finished three CONACYT (NSF in Mexico) projects in the field of Knowledge and Data Engineering, and one collaborative project with University of California Riverside. Her research interests include knowledge based system, machine learning and data mining applications, social network analysis, Petri nets, non-linear system identification and control, neural networks, system modeling and simulation, active database system, Human machine Interface, etc..