题 目（TITLE）：Theory of belief functions for data analysis and machine learning applications: review and prospects
讲 座 人（SPEAKER）: Prof. Thierry Denoeux；CNRS Heudiasyc laboratory, France
主 持 人 (CHAIR)：Prof. Liu Chenglin
时 间 (TIME)： 10:00AM, October 13 (Wednesday)
地 点 (VENUE)： The Second Meeting Room, 13th Floor
The Dempster-Shafer theory of belief functions provides a unified framework for handling both aleatory uncertainty, arising from statistical variability in populations, and epistemic uncertainty, arising from incompleteness of knowledge. An overview of both the fundamentals and some recent developments in this theory will first be presented. Several applications in data analysis and machine learning will then be reviewed, including learning under partial supervision, multi-label classification, ensemble clustering and the treatment of pairwise comparisons in sensory or preference analysis.
Thierry Denoeux graduated in 1985 from the Ecole Nationale des Ponts et Chaussées in Paris, and received a doctorate from the same institution in 1989. Currently, he is Full Professor with the Department of Information Processing Engineering at the Université de Technologie de Compiègne, France, and the deputy-director of Heudiasyc, a joint laboratory with the French National Center for Scientific Research (CNRS) . His research interests concern the theory of belief functions, fuzzy data analysis and, more generally, the management of imprecision and uncertainty in data analysis, pattern recognition and information fusion. He is the Editor-in-Chief of the Elsevier journal “International Journal of Approximate Reasoning”, and a member of the editorial board of “Fuzzy Sets and Systems”.