Predicting source gaze fixation duration: A machine learning approach

Author

Saikh, Tanik ; Bangalore, Srinivas ; Carl, Michael ; Bandyopadhyay, Sivaji

Author_Institution

CSE Dept., Jadavpur Univ., Kolkata, India

fYear

2015

fDate

3-4 March 2015

Firstpage

1

Lastpage

6

Abstract

In this paper an attempt has been made to predict the gaze fixation duration at source text words using supervised learning method, namely Support Vector Machine. The machine learning models used in the present work make use of lexical, syntactic and semantic information for predicting the gaze fixation duration. Different features are extracted from the data and models are built by combining the features. Our best set up achieves close to 50% classification accuracy.

Keywords

feature extraction; gaze tracking; image classification; learning (artificial intelligence); support vector machines; classification accuracy; feature extraction; lexical information; machine learning approach; semantic information; source gaze fixation duration prediction; source text words; supervised learning method; support vector machine; syntactic information; Accuracy; Correlation coefficient; Entropy; Feature extraction; Linear regression; Predictive models; Syntactics; Eye Tracking; Gaze fixation duration; Machine Learning; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Computing and Information Processing (CCIP), 2015 International Conference on

Conference_Location

Noida

Type

conf

DOI

10.1109/CCIP.2015.7100708

Filename

7100708