A result of Vapnik with applications

Discrete Applied Mathematics

Volumn 47, Issue 3, 1993, Pages 207-217

  Anthony, Martin ^a   Shawe Taylor, John ^b  

^a LONDON SCHOOL OF ECONOMICS   (United Kingdom)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0041805140     PISSN: 0166218X     EISSN: None     Source Type: Journal    
DOI: 10.1016/0166-218X(93)90126-9     Document Type: Article

References (15)

1. The learnability of formal concepts
2. What size net gives valid generalization
3. Learnability and the Vapnik-Chervonenkis dimension
4. Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
5. Decision-theoretic generalizations of the pac model for neural net and other learning applications
6. Generalizing the PAC model for neural net and other learning applications
7. On learning sets and functions
8. On the density of families of sets
9. Bounding sample size with the Vapnik-Chervonenkis dimension
10. A theory of the learnable
11. On the uniform convergence of relative frequencies of events to their probabilities