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dc.contributor.author Melhart, David en
dc.contributor.author Sfikas, Konstantinos en
dc.contributor.author Giannakakis, Giorgos en
dc.contributor.author Yannakakis, Georgios N. en
dc.contributor.author Liapis, Antonios en
dc.date.accessioned 2019-02-16T19:10:28Z
dc.date.available 2019-02-16T19:10:28Z
dc.date.issued 2019-02-16
dc.identifier.uri http://hdl.handle.net/11400/20233
dc.rights Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.title A Study on Affect Model Validity: Nominal vs Ordinal Labels en
heal.type conferenceItem
heal.language en
heal.access free
heal.publicationDate 2018
heal.bibliographicCitation David Melhart, Konstantinos Sfikas, Giorgos Giannakakis, Georgios N. Yannakakis and Antonios Liapis: "A Study on Affect Model Validity: Nominal vs Ordinal Labels" in Proceedings of the IJCAI workshop on AI and Affective Computing, 2018 en
heal.abstract The question of representing emotion computationally remains largely unanswered: popular approaches require annotators to assign a magnitude (or a class) of some emotional dimension, while an alternative is to focus on the relationship between two or more options.Recent evidence in affective computing suggests that following a methodology of ordinal annotations and processing leads to better reliability and validity of the model. This pa-per compares the generality of classification methods versus preference learning methods in predicting the levels of arousal in two widely used affective datasets. Findings of this initial study further validate the hypothesis that approaching affect labels as ordinal data and building models via preference learning yields models of better validity. en
heal.sponsor This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 693150 en
heal.fullTextAvailability false
heal.conferenceName 2018 IJCAI workshop on AI and Affective Computin en
heal.conferenceItemType full paper


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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες