报 告 人：Professor Thierry Denoeux

Université de Technologie de Compiègne (UTC), France

报告时间：7月8日（周五）10:00～11:30

报告地点：宝山校区计算机大楼801室

邀 请 人：岳晓冬 博士

报告摘要：

The Dempster-Shafer theory of belief function is a formal framework for modeling and reasoning with uncertainty. Different supervised and unsupervised learning algorithms based on belief functions are presented. In supervised classification, belief functions can be constructed based on distances to nearest neighbors or prototypes, and the classifier can be trained by minimizing a cost function. For clustering, we introduce the notion of credal partition, which extends hard, fuzzy, possibility and rough partitions. In a credal partition, cluster membership uncertainty is represented by belief functions. We present different algorithms for learning a credal parititon from dissimilarity or attribute data. These algorithms have been coded in the R package evclust publicly available from CRAN.

报告人简介:

Thierry Denoeux is a Full Professor (Exceptional Class) with the Department of Information Processing Engineering at the Université de Technologie de Compiègne (UTC), France. His research interests concern the management of uncertainty in intelligent systems. His main contributions are in the theory of belief functions with applications to pattern recognition, data mining and information fusion. He has published more than 200 journal and conference papers in this area. He is the Editor-in-Chief of the International Journal of Approximate Reasoning, and an Associate Editor of several journals including Fuzzy Sets and Systems and the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS).