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Journal Articles

Vapnik-Chervonenkis Generalization Bounds for Real Valued Neural Networks

Arne Hole
Publisher: Journals Gateway
Neural Computation (1996) 8 (6): 1277–1299.
DOI: https://doi.org/10.1162/neco.1996.8.6.1277
Published: 01 August 1996
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.
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