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.
Cite
Text
Kulkarni et al. "PAC Learning with Generalized Samples and an Application to Stochastic Geometry." Annual Conference on Computational Learning Theory, 1992. doi:10.1145/130385.130405Markdown
[Kulkarni et al. "PAC Learning with Generalized Samples and an Application to Stochastic Geometry." Annual Conference on Computational Learning Theory, 1992.](https://mlanthology.org/colt/1992/kulkarni1992colt-pac/) doi:10.1145/130385.130405BibTeX
@inproceedings{kulkarni1992colt-pac,
title = {{PAC Learning with Generalized Samples and an Application to Stochastic Geometry}},
author = {Kulkarni, Sanjeev R. and Tsitsiklis, John N. and Mitter, Sanjoy K. and Zeitouni, Ofer},
booktitle = {Annual Conference on Computational Learning Theory},
year = {1992},
pages = {172-179},
doi = {10.1145/130385.130405},
url = {https://mlanthology.org/colt/1992/kulkarni1992colt-pac/}
}