Computer Science

CS Colloquium Speaker: Dr. Chih-Jen Lin from the National Taiwan University

CS Colloquium Speaker: Distinguished Professor Chih-Jen Lin from the National Taiwan University

Host: Cho-Jui Hsieh

When: Wednesday, May 4th at 3:10pm

Where: 1003 Kemper Hall 

Title: Large-scale linear classification: status and challenges


Many classification techniques such as kernel methods or decision
trees are nonlinear approaches. However, linear methods of using a
simple weight vector as the model remain to be very useful for many
applications. By careful feature engineering and having data in a rich
dimensional space, the performance may be competitive with that of
using a highly nonlinear classifier. Successful application areas
include document classification and computational advertising. In the
first part of this talk, we give an overview of linear classification
by introducing commonly used formulations. We discuss optimization
techniques developed in our linear-classification package LIBLINEAR
for fast training. The flexibility over kernel methods in selecting
and employing optimization methods can be clearly seen in our
discussion. In the second part of the talk, we select a few examples
to demonstrate how linear classification is practically applied. They
range from small to big data. The third part of the talk discusses
issues in applying linear classification for big-data analytics. We
particularly demonstrate our recent work on multi-core and distributed
linear classification.


Chih-Jen Lin is currently a distinguished 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 one of the most widely used and
cited SVM packages. For his research work he has received many awards,
including the ACM KDD 2010 and ACM RecSys 2013 best paper awards. He
is an IEEE fellow, a AAAI fellow, and an ACM fellow
for his contribution to machine learning algorithms and software
design. More information about him can be found at

1131 Kemper Hall

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