نتایج جستجو برای: multiple source data

تعداد نتایج: 3255054  

2003
Abdelhamid Bouchachia

To make effective use of distributed information, it is desirable to allow coordination and collaboration among various information sources. This paper deals with clustering data emanating from different sites. The process of clustering consists of three steps: find the (local) clusters of data at each site; find (higher) clusters from the union of the distributed data sets at the central site;...

2004
Barbara Catania Anna Maddalena Maurizio Mazza Elisa Bertino Stefano Rizzi

To represent and manage data mining patterns, several aspects have to be taken into account: (i) patterns are heterogeneous in nature; (ii) patterns can be extracted from raw data by using data mining tools (a-posteriori patterns) but also defined by the users and used for example to check how well they represent some input data source (a-priori patterns); (iii) since source data change frequen...

Journal: :Frontiers in Psychology 2023

Although writing self-efficacy has been a productive line of research for several decades, no prior measure focused on students’ integrating information across multiple sources when producing an academic text. To fill this gap in existing the measurement motivation, we designed targeting extent to which students are confident that they can write text integrates content from different sources. I...

Journal: :IEEE/ACM transactions on audio, speech, and language processing 2021

The problem of multiple acoustic source localization using observations from a microphone array network is investigated in this article. Multiple signals are assumed to be window-disjoint-orthogonal (WDO) on the time-frequency (TF) domain and time delay arrival (TDOA) measurements extracted at each TF bin. A Bayesian model then proposed jointly assign different sources estimate locations. Consi...

Journal: :Zisin (Journal of the Seismological Society of Japan. 2nd ser.) 1985

Journal: :Information Sciences 2022

Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving optimization problems (DMOPs), as the used methods in DMOEAs can effectively generate a good initial population new environment. However, most of them only non-dominated solutions from previous one or two environments, which cannot fully exploit all historical information and...

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