VC dimensio

The document discusses the Vapnik-Chervonenkis (VC) dimension, which is a measure of the "power" or capacity of a learning machine or classifier. The VC dimension allows one to estimate the error of a classifier on future data based only on its training error and VC dimension. Specifically, with high probability the test error is bounded above by the training error plus an additional term involving the VC dimension. The document also introduces the concept of a classifier "shattering" a set of points, which relates to calculating the VC dimension.