Skip navigation
Skip navigation

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)


File Description SizeFormat Image
01_Copeland_Fuzzy_output_error_as_the_2014.pdf206.59 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  20 July 2017/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator