Bounding sample size with the Vapnik-Chervonenkis dimension
type: Item
instance of: scholarly article
title: Bounding sample size with the Vapnik-Chervonenkis dimension
zbMATH Open document ID: 0784.68070
DOI: 10.1016/0166-218X(93)90179-R
author: John Shawe-Taylor
author: Martin Anthony
author: Norman L. Biggs
published in: Discrete Applied Mathematics
publication date: 1993-05-16T00:00:00Z
review text: The authors give a new proof that a class is learnable in Valiant's P.A.C sense if the Vapnik-Chervonenkis dimension is finite. The new proof provides an improvement of the sample size needed.
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 167440
zbMATH Keywords: P.A.C
zbMATH Keywords: Vapnik-Chervonenkis dimension
reviewed by: Jean-Thierry Lapresté
MaRDI profile type: Publication
cites work: Learnability and the Vapnik-Chervonenkis dimension
cites work: Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
cites work: \(\epsilon\)-nets and simplex range queries
cites work: Computational limitations on learning from examples
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description: scientific article; zbMATH DE number 167440