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In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis dimension is a measure of the capacity of a set of functions that can be learned by a statistical binary classification algorithm. It is defined as the cardinality of the largest set of points that the algorithm can shatter. Wikipedia
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Apr 23, 2018 — The VC dimension of a classifier is defined by Vapnik and Chervonenkis to be the cardinality (size) of ...
VC dimension is a formal measure of bias which has played an important role in mathematical work on learnability.