Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
AnglePose: robust, precise capacitive touch tracking via 3d orientation estimation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
100,000,000 taps: analysis and improvement of touch performance in the large
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Touch behavior with different postures on soft smartphone keyboards
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
A user-specific machine learning approach for improving touch accuracy on mobile devices
Proceedings of the 25th annual ACM symposium on User interface software and technology
User-specific touch models in a cross-device context
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
User-specific touch models in a cross-device context
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
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Touch offset models which improve input accuracy on mobile touch screen devices typically require the use of a large number of training points. In this paper, we describe a method for selecting training points such that high performance can be attained with fewer data. We use the Relevance Vector Machine (RVM) algorithm, and show that performance improvements can be obtained with fewer than 10 training examples. We show that the distribution of training points is conserved across users and contains interesting structure, and compare the RVM to two other offset prediction models for small training set sizes.