Machine Learning

, Volume 25, Issue 1, pp 51–70

PAC learning of one-dimensional patterns

  • Paul W. Goldberg
  • Sally A. Goldman
  • Stephen D. Scott
Article

DOI: 10.1007/BF00115300

Cite this article as:
Goldberg, P.W., Goldman, S.A. & Scott, S.D. Mach Learn (1996) 25: 51. doi:10.1007/BF00115300

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 ofk points on the real line. Each instance is a configuration ofn points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patternsP andQ resemble each other under the Hausdorff metric if every point on one pattern is “close” to some point on the other pattern. We relate the concept class of geometric patterns to the landmark matching problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.

Keywords

PAC learning landmark matching robot navigation 
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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Paul W. Goldberg
    • 1
  • Sally A. Goldman
    • 2
  • Stephen D. Scott
    • 3
  1. 1.Dept. of Computer Science and Applied MathematicsAston UniversityBirminghamU.K.
  2. 2.Dept. of Computer ScienceWashington UniversitySt. Louis
  3. 3.Dept. of Computer ScienceWashington UniversitySt. Louis

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