A supervised learning framework in the context of multiple annotators
The increasing popularity of crowdsourcing platforms, i.e., Amazon Mechanical Turk, is changing how datasets for supervised learning are built. In these cases, instead of having datasets labeled by one source (which is supposed to be an expert who provided the absolute gold standard), we have datase...
- Autores:
-
Gil González, Julián
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Pereira
- Repositorio:
- Repositorio Institucional UTP
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utp.edu.co:11059/13832
- Acceso en línea:
- https://hdl.handle.net/11059/13832
- Palabra clave:
- Redes neuronales
Aprendizaje de máquina
Procesos Gaussianos
005 - Datos en sistemas informaticos
620 - Ingeniería y operaciones afines
Modeling languages (Computer science)
Enterprise application integration (Computer systems)
- Rights
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Álvarez Meza, Andrés MarinoGil González, JuliánDoctor en Ingeniería2021-10-28T01:57:39Z2021-11-02T19:43:42Z2021-10-28T01:57:39Z2021-11-02T19:43:42Z2021https://hdl.handle.net/11059/13832T005.30688 G463 F8566The increasing popularity of crowdsourcing platforms, i.e., Amazon Mechanical Turk, is changing how datasets for supervised learning are built. In these cases, instead of having datasets labeled by one source (which is supposed to be an expert who provided the absolute gold standard), we have datasets labeled by multiple annotators with different and unknown expertise. Hence, we face a multi-labeler scenario, which typical supervised learning models cannot tackle. For such a reason, much attention has recently been given to the approaches that capture multiple annotators’ wisdom. However, such methods residing on two key assumptions: the labeler’s performance does not depend on the input space and independence among the annotators, which are hardly feasible in real-world settings...application/pdfengPereira: Universidad Tecnológica de PereiraFacultad de IngenieríaDoctorado en IngenieríaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Redes neuronalesAprendizaje de máquinaProcesos Gaussianos005 - Datos en sistemas informaticos620 - Ingeniería y operaciones afinesModeling languages (Computer science)Enterprise application integration (Computer systems)A supervised learning framework in the context of multiple annotatorsdoctoralThesisacceptedVersionhttp://purl.org/coar/resource_type/c_db06PublicationORIGINALT005.30688 G463.pdfDocumento principalapplication/pdf3215105https://dspace7-utp.metabuscador.org/bitstreams/ff1e4937-9cff-4c44-9525-e75d569b213c/download24a0055f64e93b0f3e8c4819b282ffa9MD51CC-LICENSElicense_rdfapplication/octet-stream1223https://dspace7-utp.metabuscador.org/bitstreams/356c2d32-3853-4c4d-8fbe-b44379d59640/download7c9ab7f006165862d8ce9ac5eac01552MD52LICENSElicense.txttext/plain849https://dspace7-utp.metabuscador.org/bitstreams/e3bae1cb-3446-4a9a-bd12-77ddaa506527/downloade2e549e0a1eff8f2de922c8fd2184f09MD53TEXTT005.30688 G463.pdf.txtT005.30688 G463.pdf.txtExtracted texttext/plain353391https://dspace7-utp.metabuscador.org/bitstreams/5313b12d-d922-42b5-81ae-1c7945373387/downloadbea40cd4509e497f6567bd60da87b2aaMD56THUMBNAILT005.30688 G463.pdf.jpgT005.30688 G463.pdf.jpgGenerated Thumbnailimage/jpeg7243https://dspace7-utp.metabuscador.org/bitstreams/2326936d-af3b-48ab-a822-8c91b5fdfb8c/download777ca542505eb03013bd551a710e1ac9MD5711059/13832oai:dspace7-utp.metabuscador.org:11059/138322024-09-05 16:59:19.168http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://dspace7-utp.metabuscador.orgRepositorio de la Universidad Tecnológica de Pereirabdigital@metabiblioteca.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 |
| dc.title.spa.fl_str_mv |
A supervised learning framework in the context of multiple annotators |
| title |
A supervised learning framework in the context of multiple annotators |
| spellingShingle |
A supervised learning framework in the context of multiple annotators Redes neuronales Aprendizaje de máquina Procesos Gaussianos 005 - Datos en sistemas informaticos 620 - Ingeniería y operaciones afines Modeling languages (Computer science) Enterprise application integration (Computer systems) |
| title_short |
A supervised learning framework in the context of multiple annotators |
| title_full |
A supervised learning framework in the context of multiple annotators |
| title_fullStr |
A supervised learning framework in the context of multiple annotators |
| title_full_unstemmed |
A supervised learning framework in the context of multiple annotators |
| title_sort |
A supervised learning framework in the context of multiple annotators |
| dc.creator.fl_str_mv |
Gil González, Julián |
| dc.contributor.advisor.none.fl_str_mv |
Álvarez Meza, Andrés Marino |
| dc.contributor.author.none.fl_str_mv |
Gil González, Julián |
| dc.subject.spa.fl_str_mv |
Redes neuronales Aprendizaje de máquina Procesos Gaussianos |
| topic |
Redes neuronales Aprendizaje de máquina Procesos Gaussianos 005 - Datos en sistemas informaticos 620 - Ingeniería y operaciones afines Modeling languages (Computer science) Enterprise application integration (Computer systems) |
| dc.subject.ddc.spa.fl_str_mv |
005 - Datos en sistemas informaticos 620 - Ingeniería y operaciones afines |
| dc.subject.lemb.eng.fl_str_mv |
Modeling languages (Computer science) Enterprise application integration (Computer systems) |
| description |
The increasing popularity of crowdsourcing platforms, i.e., Amazon Mechanical Turk, is changing how datasets for supervised learning are built. In these cases, instead of having datasets labeled by one source (which is supposed to be an expert who provided the absolute gold standard), we have datasets labeled by multiple annotators with different and unknown expertise. Hence, we face a multi-labeler scenario, which typical supervised learning models cannot tackle. For such a reason, much attention has recently been given to the approaches that capture multiple annotators’ wisdom. However, such methods residing on two key assumptions: the labeler’s performance does not depend on the input space and independence among the annotators, which are hardly feasible in real-world settings... |
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2021 |
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2021-10-28T01:57:39Z 2021-11-02T19:43:42Z |
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2021-10-28T01:57:39Z 2021-11-02T19:43:42Z |
| dc.date.issued.none.fl_str_mv |
2021 |
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doctoralThesis |
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http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
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https://hdl.handle.net/11059/13832 |
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T005.30688 G463 F8566 |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 International |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Pereira: Universidad Tecnológica de Pereira |
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Facultad de Ingeniería |
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Doctorado en Ingeniería |
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