标题：Bayesian Ying-Yang System, Best Harmony Learning, and Five Action Circling
报告人：徐雷 香港中文大学Chair Professor
IEEE Fellow, IAPR Fellow， 欧洲科学院院士
国际神经网络学会领导奖、亚太神经网络学会杰出成就奖获得者
时间：5月4日下午16:00-17:00
地点：蒙民伟楼404会议室
摘要：
Proposed in 1995 and systematically developed over fifteen years, Bayesian
Ying-Yang (BYY) learning is a statistical approach for an intelligent system
via two complementary Bayesian representations of a joint distribution on the
external observation X and its inner representation R, called BYY system. A
Ying-Yang best harmony principle is proposed for learning all the unknowns in
the system, in help of an implementation featured by a five action circling.
BYY learning provides not only a general framework that accommodates typical
learning approaches from a unified perspective but also a new road that leads
to improved model selection criteria, automatic model selection during
learning, and coordinated implementation of Ying based model selection and
Yang based learning regularization. This talk introduces BYY learning
principles, implementing techniques, and typical learning algorithms, in a
comparison with other algorithms, particularly with the EM algorithm as a
benchmark. These algorithms are summarized in a unified Ying-Yang alternation
procedure with major parts in a same expression while differences simply
characterized by few options.
简历：
Lei Xu, chair professor of Chinese Univ Hong Kong, Chang Jiang Chair Professor
of Peking Univ, IEEE Fellow (2001-) and Fellow of International Association
for Pattern Recognition (2002-), and Academician of European Academy of
Sciences (2002-). He completed his Ph.D thesis at Tsinghua Univ by the end of
1986, then joined Dept. Math, Peking Univ in 1987 first as a postdoc and then
exceptionally promoted to associate professor in 1988 and to a full professor
in 1992. During 1989-93 he worked at several universities in Finland, Canada
and USA, including Harvard and MIT. He joined CUHK in 1993 as senior lecturer,
as professor in 1996 and chair professor in 2002. He has published a number
of well-cited papers on neural networks, statistical learning, and pattern
recognition, e.g., his papers got over 3200 citations according to SCI and
over 5500 citations according to Google Scholar (GS), with the first 10 papers
scored over 2000 (SCI) and 3600 (GS). One single paper has scored 750 (SCI)
and 1211 (GS). He served as associate editor for several journals, past
governor of international neural network society (INNS), a past president of A
PNNA, and a member of Fellow committee of IEEE CI Society. Also, he has
received several national and international academic awards (e.g., 1993
National Nature Science Award, 1995 INNS Leadership Award and 2006 APNNA
Outstanding Achievement Award).
