Machine Intelligence


Lab Name and Affiliation

Machine Intelligence

School of Electrical and Electronic Engineering, Yonsei University 120-749

Lab Director (or Principal Investigator)

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) in 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, 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, Dr. Toh has served as a member/co-chair/advisory board member of technical program committees for international conferences related to biometrics and artificial intelligence. He is currently an Associate Editor of IEEE Transactions on Information Forensics and Security, Pattern Recognition Letters, IET Biometrics and IEEE Biometrics Council Newsletter. He is a senior member of the IEEE.

Lab Introduction

The Machine Intelligence laboratory was established in 2005 with research focusing on Pattern Classification, Machine Learning and Biometrics. In pattern classification and machine learning, we have established closed-form solutions to minimization of classification error (MCE) and maximization of area under the receiver operating characteristic curve (AUC). These solutions are subsequently understood from data transformation perspective in terms of their relationships to well-known state-of-the-arts such as Linear Least Squares, Fisher Discriminant Analysis and Gaussian Process. Grounded on these findings, our current research focus is aiming towards a seamless feature extraction for accurate classification. In biometrics, our focus is on multibiometrics fusion and face recognition where challenges relevant to learning and classification are well exhibited.

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