Results on learnability and the Vapnik-Chervonenkis dimension

Nathan Linial*, Yishay Mansour, Ronald L. Rivest

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


The problem of learning a concept from examples in a distribution-free model is considered. The notion of dynamic sampling, wherein the number of examples examined can increase with the complexity of the target concept, is introduced. This method is used to establish the learnability of various concept classes with an infinite Vapnik-Chervonenkis (VC) dimension. An important variation on the problem of learning from examples, called approximating from examples, is also discussed. The problem of computing the VC dimension of a finite concept set defined on a finite domain is considered.

Original languageAmerican English
Title of host publicationAnnual Symposium on Foundations of Computer Science (Proceedings)
PublisherPubl by IEEE
Number of pages10
ISBN (Print)0818608773, 9780818608773
StatePublished - 1988
Externally publishedYes

Publication series

NameAnnual Symposium on Foundations of Computer Science (Proceedings)
ISSN (Print)0272-5428

Bibliographical note

Funding Information:
* This paper was prepared with support from NSF Grant DCR-8607494, AR0 Grant DAAL-03-86-K-0171, and the Siemens Corporation.


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