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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ostermann, Frank O. | |
dc.date.accessioned | 2020-02-18T02:26:56Z | - |
dc.date.available | 2020-02-18T02:26:56Z | - |
dc.date.issued | 2014 | |
dc.identifier.citation | In: Proceedings of the 18th AGILE International conference on geographic information science, 9-12 June 2015, Lisbon, Portugal. AGILE, 2015. 4 p | |
dc.identifier.uri | http://tailieuso.tlu.edu.vn/handle/DHTL/4718 | - |
dc.description.abstract | This paper introduces an approach to crowdsource the supervision of machine learning classification and regression tasks in order to process geo-social media streams. It builds on a review and comparison of four existing approaches to process geo-social media streams in order to identify specific opportunities and challenges. An original conceptual framework situates the machine learning tasks within a geoinformation processing workflow. The paper presents and discusses concrete techniques and software solutions for implementing it. Keywords: crowdsourcing, supervised machine learning, geo-social media streams, user-generated geographic content, volunteered geographic information. | |
dc.description.uri | https://agile-online.org/Conference_Paper/cds/agile_2015/shortpapers/78/78_Paper_in_PDF.pdf | |
dc.language | eng | |
dc.subject | volunteered geographic information | |
dc.subject | user-generated geographic content | |
dc.subject | geo-social media streams | |
dc.subject | supervised machine learning | |
dc.title | Hybrid geo-information processing :crowdsourced supervision of geo-spatial machine learning tasks | |
dc.type | BB | |
dc.date.update | 20181112160849.0 | |
dc.date.submitte | 130605s2014 | |
Appears in Collections: | Tài liệu mở |
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