NTU talk: Bridging the Gap between Signal Processing and Machine Learning

Title: Bridging the Gap between Signal Processing and Machine Learning
Speaker: Dr. Y.-C. Frank Wang, Academia Sinica
Time: 2:20pm, Oct 16 (Fri), 2009
Place: Room 103, CSIE building

Abstract:
The advancements of signal processing and machine learning techniques have afforded many applications which benefit people in different areas. The first part of this talk starts with some interesting examples, and explains how “signal processing” and “machine learning” people might interpret the same thing in different points of view. I will discuss why it is vital to bridge the gap between these two areas, and what can be achieved by close collaboration between people from these two communities.

Many of real-world machine learning applications involve recognition of multiple classes. However, standard classification methods may not perform well and efficiently on large-scale problems. Designing a classifier with good generalization and scalability is an ongoing research topic for machine learning researchers. Another important issue, which is typically not addressed in prior work, is the rejection of unseen false classes (not of interest). Since we cannot design a classifier by training the data from “unseen” classes, the rejection problem becomes very challenging. In the second part of this talk, I will present my proposed work, soft-decision hierarchical SVRDM (support vector representation and discrimination machine) classifier, which is to address aforementioned multiclass classification and rejection problems.

Short Biography:
Yu-Chiang Frank Wang received his B.S. in Electrical Engineering from National Taiwan University in 2001. Before pursuing his graduate study, he worked as a research assistant in the Division of Medical Engineering Research at National Health Research Institutes in Taiwan from 2001 to 2002. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University in 2004 and 2009, respectively. His research projects at CMU included the development of a military automated target recognition system, the design of a multi-modal biometric fusion system, and algorithms for data clustering and multi-class classification problems. His research and graduate study were funded by the US Army Research Office through Carnegie Mellon.

Dr. Wang is currently an assistant research fellow in the Research Center for Information Technology Innovation (CITI) at Academia Sinica, where he also holds the position as an adjunct assistant research fellow in the Institute of Information Science. His research interests span the areas of pattern recognition, machine learning, computer vision, multimedia signal processing and content analysis.