Accession Number:

ADA459600

Title:

PAC Learning with Generalized Samples and an Application to Stochastic Geometry

Descriptive Note:

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS

Report Date:

1991-06-01

Pagination or Media Count:

19.0

Abstract:

In this paper, we introduce an extension of the standard PAC model which allows the use of generalized samples. We view a generalized sample as a pair consisting of a functional on the concept class together with the value obtained by the functional operating on the unknown concept. It appears that this model can be applied to a number of problems in signal processing and geometric reconstruction to provide sample size bounds under a PAC criterion. We consider a specific application of the generalized model to a problem of curve reconstruction, and discuss some connections with a result from stochastic geometry.

Subject Categories:

  • Statistics and Probability
  • Psychology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE