Keynote Speakers

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. (http://www.ee.cityu.edu.hk/~hpyan/)

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

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.