Publication Details
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Daryl Weir, Daniel Buschek, Simon Rogers
Sparse Selection of Training Data for Touch Correction Systems In MobileHCI '13: Proceedings of the 15th International Conference on Human-Computer Interaction with Mobile Devices and Services. Munich, Germany, August 27 - 30, 2013. ACM, New York, NY, USA. doi: 10.1145/2493190.2493241 (bib) |
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. |