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

Authors:
Paul W. Goldberg;Sally A. Goldman
Affiliations:
Department 1423, Sandia National Laboratories, MS 1110, P.O. BOX 5800, Albuquerque, NM;Dept. of Computer Science, Washington University, St. Louis, MO
Venue:
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Year:
1994

Citing 7
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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.We relate the concept class of geometric patterns to the landmark recognition problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns when the negative examples are corrupted by a large amount of random misclassification noise.