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Vapnik–Chervonenkis dimension

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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

  • V. Vapnik and A. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16(2):264--280, 1971.
  • 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.
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