南京大学兼职教授杨强博士聘任仪式及报告会 报告题目： Recent Advances in Transfer Learning
时间：11月7日下午16:00-17:00
地点：蒙民伟楼109报告厅
摘要： Many existing data mining and machine learning techniques fail when tra
ining and test data have different distributions or feature spaces. In such ca
ses, we would like to reuse as much useful knowledge as possible from the old
data. This problem is known as transfer learning. In this talk, I will give a
n overview of our recent work on transfer learning. This work is done in colla
boration with colleagues in Shanghai Jiaotong University and PhD students at H
KUST. 简历： Qiang Yang is a professor at Hong Kong University of Science and Techno
logy, Department of Computer Science and Engineering. His research interests a
re AI and data mining, including machine learning, planning, case-based reason
ing. He received his PhD from the University of Maryland, College Park. He's a
member of AAAI, ACM and a senior member of the IEEE. He is also an associate
editor for the IEEE TKDE and IEEE Intelligent Systems, and WI Journals. Con
tact him at the Dept. of Computer Science and Engineering, Hong Kong Univ. of
Science and Technology, Clearwater Bay, Kowloon, Hong Kong; qyang@cse.ust.hk;
http://www.cse.ust.hk/~qyang |
