Vapnik-Chervonenkis Generalization Bounds for Real Valued Neural Networks
Arne Hole · Neural Computation · 1996
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.