Contents Intro.

With the rapid development of data acquisition, IT infrastructures,social networks and Web2.0 applications, more and more massive, heterogeneous, uncertain,dynamically changing and socialized data are generated and stored in distributedsystems. Discovering implied knowledge from data is always the topic with greatattention for data understanding, data utilization and information services,where uncertainty is ubiquitous. We adopt Bayesian network (BN), one of thepopular and important probabilistic graphical models, as the effectiveframework for representing and inferring uncertain knowledge by means ofqualitative and quantitative manners. In this report, we present our studies onacquisition, representation, inference and fusion of uncertain knowledgeimplied in data, oriented to the applications of provenance analysis and userpreference modeling.

Details

Publication year

2018

Form

電子化映像資料(1時間24分52秒)

Series title

情報科学研究科・ゼミナール講演 ; 平成29年度

Note

講演者所属: Yunnan University

講演日: 平成30年1月9日

講演場所: 情報科学研究科大講義室L1

Country of publication

Japan

Title language

English (eng)

Language of texts

English (eng)

Author information

YUE, Kun