Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/47552
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Melhart, David | - |
dc.contributor.author | Sfikas, Konstantinos | - |
dc.contributor.author | Giannakakis, Giorgos | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.contributor.author | Liapis, Antonios | - |
dc.date.accessioned | 2019-10-18T08:55:12Z | - |
dc.date.available | 2019-10-18T08:55:12Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Melhart, D., Sfikas, K., Giannakakis, G., Yannakakis, G. N., & Liapis, A. (2018). A study on affect model validity : nominal vs ordinal labels. Journal of Machine Learning Research, 86, 1-8. | en_GB |
dc.identifier.issn | 15337928 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/47552 | - |
dc.description.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 paper 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_GB |
dc.language.iso | en | en_GB |
dc.publisher | MIT Press | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Classification | en_GB |
dc.subject | Support vector machines | en_GB |
dc.subject | Computer graphics | en_GB |
dc.title | A study on affect model validity : nominal vs ordinal labels | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.publication.title | Journal of Machine Learning Research | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
A_study_on_affect_model_validity_nominal_vs_ordinal_labels_2018.pdf | 250.14 kB | Adobe PDF | View/Open |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.