PAC Learning with Generalized Samples and an Application to Stochastic Geometry

Sanjeev R. Kulkarni, Sanjoy K. Mitter, John N. Tsitsiklis, Ofer Zeitouni

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In this paper we introduce an extension of the standard probably approximately correct (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 generalized model to a problem of curve reconstruction and discuss some connections with a result from stochastic geometry.

Original languageEnglish (US)
Pages (from-to)933-942
Number of pages10
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number9
StatePublished - Sep 1993

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


  • Curves
  • PAC
  • generalized samples
  • learning
  • model
  • stochastic geometry


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