Title: A Domain Driven Approach to Transfer Learning

Speaker: Prof. Qiang Yang

Abstract:

Many existing data mining and machine learning techniques are based on the assumption that training and test data fit the same distribution. This assumption does not hold, however, as in many cases of Web mining and wireless computing when labeled data becomes outdated or test data are from a different domain with training data.  In these cases, most machine learning methods would fail in correctly classifying new and future data. It would be very costly and infeasible to collect and label enough new training data. Instead, we would like to recoup as much useful knowledge as possible from the old data. This problem is known as transfer learning. In this talk, I will describe how to best exploit domain knowledge in a domain-driven transfer learning process to enable effective knowledge transfer.  I will give examples that include action mining from sensor data as well as applications on Web image and document classification.

Bio:

Qiang Yang is a professor at Hong Kong University of Science and Technology, Department of Computer Science and Engineering and Postgraduate Director. His research interests are data mining, machine learning, AI planning and activity recognition. He received his PhD from the University of Maryland, College Park, and been a faculty member at University of Waterloo and Simon Fraser University in Canada. He’s a member of AAAI, ACM and a Fellow of IEEE.  He has also been or is an associate editor for the IEEE TKDE and IEEE Intelligent Systems, KAIS and WI Journals. Contact him at the Dept. of Computer Science, Hong Kong Univ. of Science and Technology, Clearwater Bay, Kowloon, Hong Kong; This e-mail address is being protected from spambots. You need JavaScript enabled to view it ; http://www.cse.ust.hk/~qyang.