演讲人简介：
香 港中文大學講座教授、IEEE Fellow, IAPR Fellow，歐洲科學院院士

报告内容简介：
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, with 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 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.