Fuzzy output error as the performance function for training artificial neural networks to predict reading comprehension from eye gaze
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]
|Collections||ANU Research Publications|
|Source:||Lecture Notes in Computer Science (LNCS)|
|01_Copeland_Fuzzy_output_error_as_the_2014.pdf||206.59 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.