学术讲座-Select representative data to train SVM for big data classification

发布时间:2018-11-02浏览次数:601

主题:    Select representative data to train SVM for big data  classification主讲人:   Xiaoou Li 地点:   松江校区一号学院楼140报告厅时间:   2018-11-15 13:00:00组织单位:   计算机学院

主讲人简介:

Prof. Xiaoou Li obtained B. S degree of appliedmathematics in 1991 and PhD  degree of Automatic Control in 1995 fromNortheastern University, Shenyang, P. R.  China. She has been a professor of Department ofComputer Science, The Research  and Advanced Studies Centre of the NationalPolytechnic Institute  (CINVESTAV-IPN), Mexico. She was a senior research fellowof School of  Electronics, Electrical Engineering & Computer Science,Queen's University  Belfast, UK during the school year 2006-2007 (sabbaticalleave); 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 ResearcherSystem) level 2. Dr. Li has  published more than 100 papers on internationaljournals, book chapters and  conferences. She has successfully finished threeCONACYT (NSF in Mexico) projects  in the field of Knowledge and DataEngineering, and one collaborative project  with University of California Riverside.

摘要:

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 several data selection  or reduction techniques and correspondingtraining process.