Results on learnability and the Vapnik-Chervonenkis dimension

Nathan Linial, Yishay Mansour, Ronald L. Rivest

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

Abstract

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 languageEnglish
Title of host publicationAnnual Symposium on Foundations of Computer Science (Proceedings)
PublisherPubl by IEEE
Pages120-129
Number of pages10
ISBN (Print)0818608773, 9780818608773
DOIs
StatePublished - 1988
Externally publishedYes

Publication series

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

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