Title:	A Sensitivity Measure based Generalization Error Model for Supervised Classification Problems with application in Model Selection and Feature Reduction
Date:	May 4, 2006 (Thursday)
Time:	2:30 p.m. - 3:30 p.m.
Venue:	Room 121, 1/F, Ho Sin-hang Engineering Building, The Chinese University of Hong Kong, Shatin, N.T.
Speaker:	Professor Daniel S. Yeung Chair Professor Department of Computing The Hong Kong Polytechnic University

ABSTRACT:

Generalization error model provides a theoretical support for a classifier's performance in terms of prediction accuracy. However, existing models give very loose error bounds. This explains why classification systems generally rely on experimental validation for their claims on prediction accuracy. In this talk we will revisit this problem and explore the idea of developing a new generalization error model based on the assumption that only prediction accuracy on unseen points in a neighbourhood of a training point will be considered, since it will be unreasonable to require a classifier to accurately predict unseen points "far away" from training samples. The new error model makes use of the concept of sensitivity measure for an ensemble of multiplayer feedforward neural networks (Multilayer Perceptrons or Radial Basis Function Neural Networks). Two important applications will be demonstrated, model selection and feature reduction for RBFNN classifiers. A number of experimental results using datasets such as the UCI, the 99 KDD Cup, and text categorization, will be presented.

BIOGRAPHY:

Daniel S. Yeung received the Ph.D. degree in applied mathematics from Case Western Reserve University. In the past, he has worked as an Assistant Professor of Mathematics and Computer Science at Rochester Institute of Technology, as a Research Scientist in the General Electric Corporate Research Center, and as a System Integration Engineer at TRW, all in the United States. He was the chairman of the department of Computing, The Hong Kong Polytechnic University, Hong Kong where now he is a Chair Professor. His current research interests include neural-network sensitivity analysis, data mining, Chinese computing, and fuzzy systems. He was the Chairman of IEEE Hong Kong Computer Chapter (91and 92), an associate editor for both IEEE Transactions on Neural Networks and IEEE Transactions on SMC (Part B). He is a member of the Board of Governor, a Vice President for Technical Activities, and currently a Vice President for Long Range Planning and Finance for the IEEE SMC Society. He co-founded and served as a General Co-Chair since 2002 for the International Conference on Machine Learning and Cybernetics held annually in China. He also serves as a General Co-Chair (Technical Program) of the 2006 International Conference on Pattern Recognition. He is also the founding Chairman of the IEEE SMC Hong Kong Chapter. Professor Yeung is a Fellow of the IEEE and an IEEE Distinguished Lecturer.

Enquiries: Miss Temmy So at tel 2609 8444

For more information, please refer to http://www.cse.cuhk.edu.hk/seminar