Plenary Speech III:
Speaker:
Badong Chen
Professor, Xi’an Jiaotong University, China
 Badong Chen received the B.S. and M.S. degrees in control theory and engineering from Chongqing University, in 1997 and 2003, respectively, and the Ph.D. degree in computer science and technology from Tsinghua University in 2008. He was a Postdoctoral Researcher with Tsinghua University from 2008 to 2010, and a Postdoctoral Associate at the University of Florida Computational NeuroEngineering Laboratory (CNEL) during the period October, 2010 to September, 2012. During July to August 2015, he visited the Nanyang Technological University (NTU) as a visiting research scientist. Currently he is a professor at the Institute of Artificial Intelligence and Robotics (IAIR), Xi’an Jiaotong University. His research interests are in signal processing, information theory, machine learning, and their applications in cognitive science and engineering. He has published 2 books, 3 chapters, and over 100 papers in various journals and conference proceedings. Dr. Chen is an IEEE senior member and an associate editor of IEEE Transactions on Neural Networks and Learning Systems and Journal of The Franklin Institute, and has been on the editorial board of Entropy.
Title: Information Theoretic Learning
Abstract:
Information theoretic measures, such as entropy, divergence and mutual information can be used as an efficient optimization cost in machine learning and signal processing since they can capture higher order statistics of the data. Many numerical examples have shown the superior performance of information theoretic learning (ITL). This talk will give a brief overview of ITL. Its applications to robust learning and adaptive signal processing in presence of outliers or impulsive noises will be discussed. |