Time: 15:50-18:00, November 8, 2008
Abstract:
Support vector machines (SVM) and kernel methods are now important
machine learning techniques. In this tutorial, we first introduce
some basic concepts such as maximal margin, kernel mappings, and primal
dual relationships. We then discuss the training by solving
optimization problems and the selection of parameters. Finally, we
briefly mention some new research issues.
This talk is suitable for both researchers as well as practitioners.
Short bio of the speaker:
Chih-Jen Lin is currently a professor at the Department of Computer
Science, National Taiwan University. He obtained his B.S. degree from
National Taiwan University in 1993 and Ph.D. degree from University of
Michigan in 1998. His major research areas include machine learning,
data mining, and numerical optimization.
He is best known for his work on support vector machines (SVM) for
data classification. His software LIBSVM is the most widely used and
cited SVM package in the world. Nearly all major companies apply his
software for classification and regression applications. He has
received many awards for his research work. The most recent one is the
ACM KDD 2010 best research paper award. More information about him and his
software tools can be found at
http://www.csie.ntu.edu.tw/~cjlin