国际SCI期刊IJAR主编Thierry Denoeux教授学术报告通知

来源:信息科学与技术学院  作者:李天瑞  日期:2018-08-01  点击数:218

西南交通大学

“创源”大讲堂研究生学术讲座

报告题目:A fresh look at some Machine Learning techniques from the perspective of Dempster-Shafer theory

报告人:Thierry Denoeux教授(国际 SCI 期刊 IJAR主编,法国贡比涅技术大学)

报告时间:2018 年 8月10日(星期五)上午11:00

报告地点:西南交通大学犀浦校区9431#

主持人:李天瑞 教授

讲座内容简介:The Dempster-Shafer theory of belief functions is a formal framework for modeling and reasoning with uncertainty. It is based on the representation of independent pieces of evidence by belief functions, and on their combination by an operator called Dempster’s rule. In this talk, we show that the weighted sum and softmax operations performed in logistic regression classifiers and, for instance, in the output layer of feedforward neural networks can be interpreted in terms of evidence aggregation using Dempster's rule of combination. From that perspective, the output probabilities computed by such classifiers (including also support vector machines) can be seen as being derived from some belief functions, which can be laid bare and used for decision making or classifier fusion. This finding suggests that the links between machine learning and belief functions are closer than is usually assumed, and that Dempster-Shafer theory provides a suitable framework for developing new machine learning algorithms.

主讲人简介:

Thierry Denoeux is a Full Professor (Exceptional Class) with the Department of Information Processing Engineering at the Université de Technologie de Compiègne (UTC), France. He has been deputy director of the Heudiasyc research Lab (UMR 7253) from 2008 to March 2014 and a Vice President of the Scientific Council of UTC in 2012-2014. He is the scientific coordinator of the Laboratory of Excellence “Technological Systems of Systems”. Since 2016, he is an ‘Overseas Talent’ visiting professor at Beijing University of Technology. His research interests concern reasoning and decision-making under uncertainty and, more generally, the management of uncertainty in intelligent systems. His main contributions are in the theory of belief functions with applications to statistical inference, pattern recognition, machine learning and information fusion. He is the author of more than 200 papers in journals and conference proceedings and he has supervised 30 PhD thesis. He is the Editor-in-Chief of the International Journal of Approximate Reasoning (Elsevier), and an Associate Editor of several journals including 'Fuzzy Sets and Systems’ and 'International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems’.

 

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                                     主办:研究生院

承办:信息科学与技术学院