초록
Given a domain X and a collection H of functions h : X → {0, 1}, the Vapnik-Chervonenkis (VC) dimension of H measures its complexity in an appropriate sense. In particular, the fundamental theorem of statistical learning says that a hypothesis class with finite VC-dimension is PAC learnable. Recent work by Fitzpatrick, Wyman, the fourth and seventh named authors studied the VC-dimension of a natural family of functions ℋ'2t(E) : 𝔽2q → {0, 1}, corresponding to indicator functions of circles centered at points in a subset E ⊆ 𝔽2q. They showed that when |E| is large enough, the VC-dimension of ℋ'2t(E) is the same as in the case that E = 𝔽2q. We study a related hypothesis class, ℋdt(E), corresponding to intersections of spheres in 𝔽dq, and ask how large E ⊆ 𝔽dq needs to be to ensure the maximum possible VC-dimension. We resolve this problem in all dimensions, proving that whenever |E| ≥ Cdqd-1/(d-1) for d ≥ 3, the VC-dimension of ℋdt(E) is as large as possible. We get a slightly stronger result if d = 3: this result holds as long as |E| ≥ C3q7/3. Furthermore, when d = 2 the result holds when |E| ≥ C2q7/4.