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学术报告:Nonlinear Integrals and Their Applications in Data Science

发布时间:2018年05月09日 09:09      访问次数:

报告题目:Nonlinear Integrals and Their Applications in Data Science

报告人:王震源(教授、博导,美国内布拉斯加大学(Omaha)终身教授)

报告时间:2018511日(星期五)下午300

报告地点:理学院章辉楼442学术报告厅

联系人:谢加良副教授

报告摘要:In information fusion, regarding the set of considered predictive attributes in a data base as the universal set, nonadditive set functions (also called nonlinear measures or fuzzy measures) defined on its power set can effectively describe the interaction among the contribution rates from various predictive attributes towards a given target, which can be regarded as a specified objective attribute. Relevantly, the classical linear aggregation tool, weighted sum, which can be expressed as a linear integral defined on the universal set, should be generalized to be some type of nonlinear integrals. Using nonlinear integrals, some classical models in data mining, such as the multiregression and the classification, can be generalized as well. They may be widely applied in bioinformatics, medical statistics, economics, forecast, decision making et al. Facing various challenges from big data, these nonlinear models may have relevant generalizations, adjustments, improvements, and deformations.

报告人简介:王震源教授主要研究非可加测度、非线性积分在数据挖掘中的应用,曾获河北省科技进步一等奖(1985)、国家科委和劳动人事部颁发的国家级具有突出贡献的中青年科技专家称号(1986)ISI (美国科学信息研究院, SCI发布者)的经典引文奖(2000)、美国内布拉斯加大学杰出研究和创造性工作奖(2007)等奖励和荣誉称号。他已在国际权威和重要期刊Fuzzy Sets and Systems等发表科学论文100余篇,并出版专著3部。目前是Fuzzy Sets and Systems等四个国际ESI期刊的编委或副主编。

 

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