摘要：
In this talk, we shall first go through a brief account on important
developments in pattern classification and subsequently zoom in to a
new branch of linear methods for classification. In view of the
discrepancy between the frequently adopted least-squares error measure
and the actual classification error count needed, we seek a
classification error formulation for direct cost minimization. By
approximating the nonlinear counting step function using a quadratic
link, we show that the classification error rate minimization is
deterministically solvable. The approximation is further shown to be
useful for Area Under ROC (AUC) optimization in multibiometric fusion.
Based on a polynomial model and a single-hidden-layer feedforward
network (SLFN) for learning, we provide extensive numerical evidences
to support the proposal.

简历：
Kar-Ann Toh is a full professor in the School of Electrical and
Electronic Engineering at Yonsei University, South Korea. He received
the PhD degree from Nanyang Technological University (NTU), Singapore.
He worked for two years in the aerospace industry prior to his
post-doctoral appointments at research centres in NTU from 1998 to
2002. He was affiliated with Institute for Infocomm Research in
Singapore from 2002 to 2005 prior to his current appointment in Korea.
His research interests include biometrics, pattern classification,
optimization and neural networks. He is a co-inventor of a US patent
and has made several PCT filings related to biometric applications.
Besides being an active member in publications (PAMI, Machine
Learning, Neural Computation etc), Dr. Toh has served as a member of
technical program committee for international conferences related to
biometrics and artificial intelligence. He is currently an associate
editor of Pattern Recognition Letters and a senior member of the IEEE.