Keynote Speeches

Introduction & Speech Abstracts

Hong Yan

Hong Yan received his Ph.D. degree from Yale University. He was professor of imaging science at the University of Sydney and currently is chair professor of computer engineering at City University of Hong Kong. He was elected an IAPR fellow for contributions to document image analysis and an IEEE fellow for contributions to image recognition techniques and applications. Professor Yan was a Distinguished Lecturer of IEEE SMC Society during 2000 to 2015. He received the 2016 Norbert Wiener Award from IEEE SMC Society for contributions to image and biomolecular pattern recognition techniques. (

Professor Yan's research interests include:
Bioinformatics: Genomic data analysis; Structural biology
Image processing: Biomedical imaging; Document imaging
Pattern recognition: Clustering and biclustering; Human face recognition and animation

Dong Xu

Dong Xu is Chair in Computer Engineering at the School of Electrical and Information Engineering, The University of Sydney, Australia. He received the B.Eng. and PhD degrees from University of Science and Technology of China, in 2001 and 2005, respectively. While pursuing the PhD degree, he worked at Microsoft Research Asia and The Chinese University of Hong Kong for more than two years. He also worked as a postdoctoral research scientist at Columbia University from 2006 to 2007 and a faculty member at Nanyang Technological University from 2007 to 2015. His current research interests include computer vision, multimedia, machine learning and biomedical image analysis. His group has developed new machine learning methods for various vision and big data analytics related applications including Internet vision and social media (i.e., large scale image/video retrieval, visual recognition using massive Web data), biometrics (i.e., face recognition and tagging, human gait recognition and person re-identification), video analysis and medical image analysis. He has published more than 100 papers in IEEE Transactions and top tier conferences including CVPR, ICCV, ECCV, ICML, ACM MM and MICCAI. His co-authored work (with his former PhD student Lixin Duan) received the Best Student Paper Award in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) in 2010. His coauthored work (with his former PhD student Lin Chen) won the IEEE Transactions on Multimedia Prize Paper Award in 2014. When working at Nanyang Technological University, he received several research projects from Singapore National Research Foundation, A*STAR, Singapore Ministry of Education, Microsoft Research Asia and Rolls-Royce Plc. He is on the editorial boards of IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), IEEE Transactions on Neural Networks and Learning Systems (T-NNLS) and IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT). Know more.

Tuan Pham

Tuan D. Pham is Professor of Biomedical Engineering at Linkoping University, University Hospital Campus, Linkoping, Sweden. Prior to the current position, he was appointed as Professor and Leader of the Aizu Research Cluster for Medical Engineering and Informatics, and the Medical Image Processing Lab, both at the University of Aizu, Japan. Before his appointments in Japan, he was the Bioinformatics Research Group Leader at the University of New South Wales, Canberra, Australia. He has been an Editorial Member and Associate Editor of Pattern Recognition (Elsevier), served as Guest Editor of Computer Methods and Programs in Biomedicine (Elsevier), Computers in Medicine and Biology (Elsevier), BioMedical Engineering OnLine (BioMed Central), and Associate Editor of IEEE Engineering in Medicine and Biology Conference series. Dr. Pham has published extensively on pattern recognition, image processing, and time-series analysis in medicine, biology, and mental health.

Alan Liew

ALAN WEE-CHUNG LIEW is currently an Associate Professor with the School of Information & Communication Technology, Griffith University, Australia. Prior to joining Griffith University in 2007, he was an Assistant Professor at the Department of Computer Science and Engineering, Chinese University of Hong Kong, and Senior Research Fellow at the Department of Electronic Engineering, City University of Hong Kong. His research interest is in the field of medical imaging, computer vision, machine learning, pattern recognition, and bioinformatics. He has published extensively in these areas and is the author of one book and more than 150 book chapters, journal and conference papers, and holds two international patents. He has engaged actively in professional activities such as on the technical program committee of many conferences, on several journal editorial boards (including AE for IEEE Transactions on Fuzzy Systems), as assessor for Australian Research Council and HK Research Grant Council, and as reviewer for many international conferences and journals. He is a senior member of IEEE since 2005.(website)

Title of Speech: Ensemble learning: a multi-classifier framework for machine learning

Abstract: In supervised learning, a learning algorithm creates a classifier with a hypothesis about the relationship between feature X and label Y . However, different learning algorithms could produce different classification outputs, and there is no single learning algorithm that could perform well on all data sources. Experiments have shown that simple algorithms like K Nearest Neighbor could in some cases produce better accuracy compared with more sophisticated approaches such as decision tree or random forest. In this talk, I will discuss the ensemble learning framework, where a set of learners are used to produce a classification result that is better than any single classifier in the ensemble. I will discuss two kinds of ensemble architectures, and how they can help improve classification performance. I will also discuss our recent work in online ensemble learning.