Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

1995-01-01

Filename

WUCS-95-10.PDF

Technical Report Number

WUCS-95-10

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 on 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. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patterns P and Q resemble each othe runder 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 recognition 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.

Comments

Permanent URL: http://dx.doi.org/10.7936/K73R0R36

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