PAC learning with generalized samples and an application to stochastic geometry

S. R. Kulkarni, S. K. Mitter, J. N. Tsitsiklis, O. Zeitouni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we introduce an extension of the standard PAC learning 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 model to a problem of curve reconstruction, and discuss some connections with a result from stochastic geometry.

Original languageEnglish (US)
Title of host publicationProceedings of the Fifth Annual ACM Workshop on Computational Learning Theory
PublisherPubl by ACM
Pages172-179
Number of pages8
ISBN (Print)089791497X, 9780897914970
DOIs
StatePublished - 1992
EventProceedings of the Fifth Annual ACM Workshop on Computational Learning Theory - Pittsburgh, PA, USA
Duration: Jul 27 1992Jul 29 1992

Publication series

NameProceedings of the Fifth Annual ACM Workshop on Computational Learning Theory

Other

OtherProceedings of the Fifth Annual ACM Workshop on Computational Learning Theory
CityPittsburgh, PA, USA
Period7/27/927/29/92

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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