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Fuzzy output error as the performance function for training artificial neural networks to predict reading comprehension from eye gaze

Copeland, Leana; Gedeon, Tamas (Tom); Mendis, B Sumudu


Imbalanced data sets are common in real life and can have a negative effect on classifier performance. We propose using fuzzy output error (FOE) as an alternative performance function to mean square error (MSE) for training feed forward neural networks to overcome this problem. The imbalanced data sets we use are eye gaze data recorded from reading and answering a tutorial and quiz. The goal is to predict the quiz scores for each tutorial page. We show that the use of FOE as the performance...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Journal article
Source: Lecture Notes in Computer Science (LNCS)
Access Rights: Open Access


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