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Vapnik–Chervonenkis theory

 Home Mathematics Fields of mathematics Applied mathematics Theoretical computer science Computational learning theory
 0 By others on same topic  0 Discussions  1970-01-01  See my version
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.

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