A supervised learning framework in the context of multiple annotators

dc.contributor.advisor Álvarez Meza, Andrés Marino
dc.contributor.author Gil González, Julián
dc.creator.degree Doctor en Ingeniería spa
dc.date.accessioned 2021-10-28T01:57:39Z
dc.date.accessioned 2021-11-02T19:43:42Z
dc.date.available 2021-10-28T01:57:39Z
dc.date.available 2021-11-02T19:43:42Z
dc.date.issued 2021
dc.description.abstract 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... spa
dc.format application/pdf spa
dc.identifier.local T005.30688 G463 F8566 spa
dc.identifier.uri https://hdl.handle.net/11059/13832
dc.language.iso eng spa
dc.publisher Pereira: Universidad Tecnológica de Pereira spa
dc.publisher.department Facultad de Ingeniería spa
dc.publisher.program Doctorado en Ingeniería spa
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRights openAccess spa
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Redes neuronales spa
dc.subject Aprendizaje de máquina spa
dc.subject Procesos Gaussianos spa
dc.subject.ddc 005 - Datos en sistemas informaticos spa
dc.subject.ddc 620 - Ingeniería y operaciones afines spa
dc.subject.lemb Modeling languages (Computer science) eng
dc.subject.lemb Enterprise application integration (Computer systems) eng
dc.title A supervised learning framework in the context of multiple annotators spa
dc.type doctoralThesis spa
dc.type.hasVersion acceptedVersion spa
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