Bounding sample size with the Vapnik-Chervonenkis dimension

Discrete Applied Mathematics

Volumn 42, Issue 1, 1993, Pages 65-73

  Shawe Taylor, John ^a   Anthony, Martin ^a   Biggs, N L ^b  

^a UNIVERSITY OF LONDON   (United Kingdom)

^b LONDON SCHOOL OF ECONOMICS   (United Kingdom)

Author keywords

Learning; Probably Approximately Correct (PAC); sample; Vapnik Chervonenkis dimension

Indexed keywords

EID: 38249005514     PISSN: 0166218X     EISSN: None     Source Type: Journal    
DOI: 10.1016/0166-218X(93)90179-R     Document Type: Article

References (6)

1. Learnability and the Vapnik-Chervonenkis dimension
2. Quantifying inductive bias, AI learning algorithms and Valiant's learning framework
3. ε-nets and simplex range queries
4. Computational limits on learning from examples
5. A theory of the learnable
6. On the uniform convergence of relative frequencies of events to their probabilities