 |
|
|
|
|
|
|
|
Joint NTU & PREMIA Seminar Talk by Prof. Xu Lei |
|
|
|
|
Wednesday, 31 December 2008 |
We are pleased to invite you to the following seminar: Bayesian Ying Yang Harmony Learning, by Prof. Xu Lei (IEEE Fellow, IAPR Fellow, Member of European Academy of Sciences) on 12 Jan 2009 (Monday). The seminar is jointly organized by Nanyang Technological University (NTU), and Pattern Recognition and Machine Intelligence Association (PREMIA).
Title: Bayesian Ying Yang Harmony Learning
Speaker: Prof. Xu Lei (IEEE Fellow, IAPR Fellow, Member of European Academy of Sciences)
Date: 12 Jan 2009 (Monday)
Time: 2:30pm – 3:30pm
Venue: SCE Meeting Room (N4-02a-35), NTU (Building N4 -- Level 2, Section A -- Room# 35, Map: http://www.street-directory.com/ntu/campus.cgi?sizex=600&sizey=600&star=&ismap=1&x=1630&y=1681&level=1?300,299#atas)
ABSTRACT
There are two key challenges for statistical learning. One is finding appropriate mathematical representations to suit various dependence structures underlying the world, for which many learning models have been studied in past decades. The other is getting a good theory for seeking a model with an appropriate sale or complexity to learn reliable structures underlying a finite size of samples. Conventionally, a number of candidate models in different scales are enumerated, with unknown parameters estimated under the maximum likelihood (ML) principle. Thereafter, one of typical learning theories, being different from ML, is applied to select the candidate in a best scale. However, not only this two-phase implementation needs a vast computing cost, but also each of these typical approaches can provide a rough estimate only. Bayesian Ying Yang (BYY) system jointly considers two types of learning for interpreting what are observed from its world and for skills of solving problems encountered in the world, which provides a general framework for a number of existing typical learning models. The best harmony principle provides a general guideline for making parameter learning and model selection jointly. Particularly, the best Ying-Yang harmony leads to not only a criterion that outperforms typical model selection criteria in a two-phase implementation, but also an automatic model selection on several typical learning tasks with an appropriate model scale obtained automatically during parameter learning while with computing cost saved significantly. Also, degenerated cases return to several existing theories, e.g., AIC and variants, marginal likelihood type Bayesian (BIC, MDL, etc), variational Bayes. This talk will provide an introduction of BYY system and best harmony learning, with links to several existing learning models and theories and with experimental results on several typical problems in machine learning and pattern recognition.
BIOGRAPHY
Lei Xu is a chair professor of Chinese Univ Hong Kong (2002-), a Chang Jiang Chair Professor of Peking Univ, an IEEE Fellow (2001-) and a Fellow of International Association for Pattern Recognition (2002-), and a member of European Academy of Sciences (2002-). He completed his PhD thesis at Tsinghua Univ by the end of 1986, then joined Peking Univ in 1987, and further promoted exceptionally to an associate professor in 1988. During 1989-93, he worked at several universities in Europe and North America, including Harvard and MIT. He joined CUHK in 1993 as senior lecturer, became professor in 1996 and took the current position since 2002. Prof. Xu has published a number of well-cited papers in the literatures of neural networks, statistical learning, and pattern recognition, e.g., his papers got over 1800 citations according to SCI-Expended (SCI-E) and over 4000 citations according to Google Scholar (GS), with his 10 most frequently cited papers scored near 1100 (SCI-E) and 3000 (GS). Among them, one single his paper has scored 380 (SCI-E) and 1041 (GS). He served as associate editor for several journals and as general chair or program committee chair of a number of international conferences. He also served as a past governor of international neural network society (INNS), a past president of Asian-Pacific Neural Networks Assembly (APNNA), and a member of Fellow committee of IEEE Computational Intelligence Society, as well as a nominator for Kyoto prize. Moreover, he has received several Chinese national academic awards (including 1993 National Nature Science Award) and international awards (including 1995 INNS Leadership Award and the 2006 APNNA Outstanding Achievement Award).
|
|
|
|
|
|
|
|
