56

The VC-Dimension of Similarity Hypotheses Spaces

Abstract

Given a set XX and a function h:X{0,1}h:X\longrightarrow\{0,1\} which labels each element of XX with either 00 or 11, we may define a function h(s)h^{(s)} to measure the similarity of pairs of points in XX according to hh. Specifically, for h{0,1}Xh\in \{0,1\}^X we define h(s){0,1}X×Xh^{(s)}\in \{0,1\}^{X\times X} by h(s)(w,x):=1[h(w)=h(x)]h^{(s)}(w,x):= \mathbb{1}[h(w) = h(x)]. This idea can be extended to a set of functions, or hypothesis space H{0,1}X\mathcal{H} \subseteq \{0,1\}^X by defining a similarity hypothesis space H(s):={h(s):hH}\mathcal{H}^{(s)}:=\{h^{(s)}:h\in\mathcal{H}\}. We show that vcdimension(H(s))Θ(vcdimension(H)){{vc-dimension}}(\mathcal{H}^{(s)}) \in \Theta({{vc-dimension}}(\mathcal{H})).

View on arXiv
Comments on this paper