Examinando por Tema "Aprendizaje de máquina"
Resultados por página
Opciones de clasificación
DocumentoA supervised learning framework in the context of multiple annotators(Pereira: Universidad Tecnológica de Pereira, 2021) Gil González, Julián ; Álvarez Meza, Andrés MarinoThe 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...