Short Paper

Touch offset models which improve input accuracy on mobile touch screen devices typically require the use of a large num- ber 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 im- provements can be obtained with fewer than 10 training ex- amples. 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.