Cost-Sensitive Concept Learning of Sensor Use in Approach Ad Recognition

Abstract

This chapter explores a prototype learning method that complements recent work in incremental learning by considering the role of external costs arising from realistic environmental assumptions. It would be logical to use machine learning techniques to develop a system that builds an efficient recognition process in response to available sensors and the perceptual qualities of objects encountered when aiming for a reduction of laborious encoding process. This learning-from-examples task is similar to others that machine learning has addressed save for two salient differences, both of which hinge on the cost aspects of sensing and acting. Observations are virtually very large—each object may be described by the results of the many instantiations of all possible sensing procedures. As sensing procedures have different costs, that is, different execution times, the ability of a particular sensor feature to discriminate between appropriate actions must be balanced against the cost of its corresponding sensing procedure.