Learning from a population of hypotheses

Michael Kearns, H. Sebastian Seung

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

7 Scopus citations

Abstract

A population learning model is introduced. In this model a population learner is provided with an oracle that on each call produces a function that is consistent with an independent random sample of the unknown target function. Thus, each call to the hypothesis oracle causes a new sample of m random examples to be drawn, and for a function consistent with these m examples to be returned to the population learner.

Original languageEnglish (US)
Title of host publicationProc 6 Annu ACM Conf Comput Learn Theory
PublisherPubl by ACM
Pages101-110
Number of pages10
ISBN (Print)0897916115, 9780897916110
DOIs
StatePublished - 1993
Externally publishedYes
EventProceedings of the 6th Annual ACM Conference on Computational Learning Theory - Santa Cruz, CA, USA
Duration: Jul 26 1993Jul 28 1993

Publication series

NameProc 6 Annu ACM Conf Comput Learn Theory

Other

OtherProceedings of the 6th Annual ACM Conference on Computational Learning Theory
CitySanta Cruz, CA, USA
Period7/26/937/28/93

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Kearns, M., & Seung, H. S. (1993). Learning from a population of hypotheses. In Proc 6 Annu ACM Conf Comput Learn Theory (pp. 101-110). (Proc 6 Annu ACM Conf Comput Learn Theory). Publ by ACM. https://doi.org/10.1145/168304.168317