VC dimension

Vapnik Chervonenkis dimension (or VC dimension) is a measure of the capacity of a learning algorithm. It is one of the core concepts in statistical learning theory. It was originally defined by Vladimir Vapnik and Alexey Chervonenkis.

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References

• A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM, 36(4):929--865, 1989.