5/1 (三) 碩博論文研討—多媒體與生醫工程(英), 演講資料如下, 歡迎蒞臨

時　間：5月1日 星期三 13:20 ~ 15:10

地　點：工程四館027教室

演講者：Prof. Hsuan-Tien Lin ,  Dept. of CSIE, National Taiwan University.

講   題：Label Space Coding for Multi-label Classification.

演講大綱：
Multiclass classification is an important problem in machine learning. It can be used in a variety of applications, such as organizing documents to different categories automatically. Multi-label classification is an extension of multi-class classification --- the former allows a set of labels to be associated with an instance while the latter allows only one. For instance, a document may belong to both the "politics" and "health" class if it is about the National Health Insurance. Many other similar applications arise in domains like text mining, vision, or bio-informatics. In this talk, we discuss a coding view about the output (label) space of multi-label classification. The view represents each set of possible labels as a (fixed-length) binary string. We discuss the close connection between the binary-string representation and the coding theory. In particular, we demonstrate three novel research directions based on the connection: data compression (source coding), error correction (channel coding), and learnable data compression (conditional source coding). We discuss two algorithms that systematically compresses the label space for more efficient computation, and another algorithm that systematically expands the label space for better performance. The talk comes from some joint works with Farbound Tai (Neural Computation, 2012), Chun-Sung Ferng (ACML, 2011) and Yao-Nan Chen (NIPS, 2012). It is self-contained and assumes only basic background in machine learning and coding theory.

演講者經歷：
Prof. Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008, and has been an associate professor since August 2012.

Prof. Lin won the outstanding teaching award from the university in 2011. He co-authored the introductory machine learning textbook "Learning from Data." His research interests include theoretical foundations of machine learning, studies on new learning problems, and improvements on learning algorithms. He received the 2012 K.-T. Li Young Researcher Award from the ACM Taipei Chapter, and co-led the teams that won the third place of KDDCup 2009 slow track, the champion of KDDCup 2010, the double-champion of the two tracks in KDDCup 2011, and the champion of track 2 in KDDCup 2012. He is currently serving as the Secretary General of Taiwanese Association for Artificial Intelligence.


主持人：林奕成教授