Vapnik–Chervonenkis theory
= Vapnik–Chervonenkis theory
{wiki=Vapnik–Chervonenkis_theory}
Vapnik–Chervonenkis (VC) theory is a fundamental framework in statistical learning theory developed by Vladimir Vapnik and Alexey Chervonenkis in the 1970s. The theory provides insights into the relationship between the complexity of a statistical model, the training set size, and the model's ability to generalize to unseen data.