Sparse selection of training data for touch correction systems

Weir, D., Rogers, S. and Buschek, D. (2013) Sparse selection of training data for touch correction systems. In: MobileHCI 2013, Munich, Germany, 27-30 Aug 2013, pp. 1-4.

Abstract

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rogers, Dr Simon
• Authors: Weir, D., Rogers, S., and Buschek, D.
• College/School: University Services > IT Services > Computing Service