C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Predicting user intentions in graphical user interfaces using implicit disambiguation
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Preface: Fitts' law 50 years later: Applications and contributions from human-computer interaction
International Journal of Human-Computer Studies - Special issue: Fitts law 50 years later: Applications and contributions from human-computer interaction
Fitts' law and expanding targets: Experimental studies and designs for user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
Endpoint prediction using motion kinematics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Comet and target ghost: techniques for selecting moving targets
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Moving target selection in 2D graphical user interfaces
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
23rd French Speaking Conference on Human-Computer Interaction
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Novel interaction techniques have been developed to address the difficulties of selecting moving targets. However, similar to their static-target counterparts, these techniques may suffer from clutter and overlap, which can be addressed by predicting intended targets. Unfortunately, current predictive techniques are tailored towards static-target selection. Thus, a novel approach for predicting user intention in moving-target selection tasks using decision-trees constructed with the initial physical states of both the user and the targets is proposed. This approach is verified in a virtual reality application in which users must choose, and select between different moving targets. With two targets, this model is able to predict user choice with approximately 71% accuracy, which is significantly better than both chance and a frequentist approach.