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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

August 15, 1996, Vol. 8, No. 6, Pages 1277-1299
(doi: 10.1162/neco.1996.8.6.1277)
© 1996 Massachusetts Institute of Technology
Vapnik-Chervonenkis Generalization Bounds for Real Valued Neural Networks
Article PDF (959.86 KB)
Abstract

We show how lower bounds on the generalization ability of feedforward neural nets with real outputs can be derived within a formalism based directly on the concept of VC dimension and Vapnik's theorem on uniform convergence of estimated probabilities.