關於信息科學與工程學院學術報告的通知

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關於信息科學與工程學院學術報告的通知

2017-10-15 16:03 (點擊： 457)

報告題目：new models for data analysis based on nonlinear integrals

報告人：王震源教授

報告時間：2017年10月19日(週日)下午3點

報告地點：信息館401學術報告廳

報告人簡介：王震源1962年畢業於復旦大學數學系，1991年獲美國紐約州立大學(binghamton)博士學位。

從1962年開始，王震源任教於河北大學。1979至1981年，以訪問學者身份在法國巴黎第六大學概率計算實驗室和人工智能實驗室從事非可加測度和非線性積分的研究。回河北大學後，歷任副教授(1983-1986)、教授(1986-2017)、數學系系主任(1985-1990)。自1989年起，先後在美國賓厄姆頓大學(suny) 系統科學系、新墨西哥州立大學數學系、得克薩斯大學 (el paso) 數學系、以及香港中文大學計算機科學和工程學系分別任客座教授/研究員。王震源自2017年起任教於美國內布拉斯加大學(omaha)，現為該校數學系終身教授。

王震源曾獲河北省科技進步一等獎(1985)、國家科委和勞動人事部頒發的國家級具有突出貢獻的中青年科技專家稱號(1986)、isi (美國科學信息研究院, sci發佈者)的經典引文獎(2017)、美國內布拉斯加大學傑出研究和創造性工作獎(2017)等獎勵和榮譽稱號。他已發表科學論文一百五十餘篇，並出版三部專著：fuzzy measure theory(plenum，1992)、generalized measure theory (springer, 2017)、nonlinear integrals and their applications in data mining (world scientific，2017)。他是fuzzy sets and systems等四個國際雜誌的編委或副主編。王震源曾任第七、八、九屆全國政協委員。

報告簡介：in information fusion, regarding the set of considered predictive attributes (in classification, called feature attributes) in a data base as the universal set, nonadditive set functions defined on its power set can effectively describe the interaction among the contribution rates from various predictive attributes towards the fusing target, which can be regarded as a specified objective attribute. such type of interaction is totally different from the traditional statistical correlationship. 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 nonlinear integral. the choquet integral, the upper integral, and the lower integral are the common types of nonlinear integrals. data mining is just an inverse problem of information fusion. using nonlinear integrals, some classical models in data mining, such as the multiregression and the classification, can be generalized as well. once the necessary data set is available, the values of unknown parameters in these nonlinear models can be optimally determined through some soft computing techniques, including genetic algorism and pseudo gradient search, approximately. since the above-mentioned interaction can be elaborately captured, the introduced new nonlinear models are significant and powerful in practice. they may be widely applied in bioinformatics, medical statistics, economics, forecast, decision making et al. in face of various challenges from big data, these nonlinear models may have relevant generalizations, adjustments, improvements, and deformations.

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信息科學與工程學院

2017年10月15日