Learning one-dimensional geometric patterns under one-sided random misclassification noise

Abstract

Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration of k points in the real line. Each instance is a configuration of n points on the real line, where it is labeled according to whether or not it visually resembles the target pattern.