@inproceedings{60d8cd23cc874ac9a5f6c64bcdad3ac3,
title = "PAC learning with generalized samples and an application to stochastic geometry",
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.",
author = "Kulkarni, \{S. R.\} and Mitter, \{S. K.\} and Tsitsiklis, \{J. N.\} and O. Zeitouni",
year = "1992",
doi = "10.1145/130385.130405",
language = "English (US)",
isbn = "089791497X",
series = "Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory",
publisher = "Publ by ACM",
pages = "172--179",
booktitle = "Proceedings of the 5th Annual Workshop on Computational Learning Theory, COLT 1992",
note = "Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory ; Conference date: 27-07-1992 Through 29-07-1992",
}