@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 Fifth Annual ACM Workshop on Computational Learning Theory",

note = "Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory ; Conference date: 27-07-1992 Through 29-07-1992",

}