Let's try to generalize the $VC$-dimension (of the class of hyperplanes) to include accuracy/error. Let $S$ be a set of points in $R^d$ and $t$ in $[0,1]$. We say that the class of hyperplanes $t$-shatters $S$ if for every binary labeling of the points in $S$, there exists some hyperplane which separates $S$ with accuracy at least $t$ (i.e., at least $t*|S|$ of the points in $S$ are classified correctly by the hyperplane). We then define the $VC(t)$-dimension of the class of hyperplanes (in a feature space of dimension $d$) to be the size of the largest set $S$ which the hyperplanes $t$-shatter, i.e.,

$VC(t) = max_{S \subset R^d}{|S|}$, subject to the constraint that the hyperplanes $t$-shatter $S$.

For example, the usual VC-dimension is $VC(1)$. So $VC(1)=d+1$. $VC(t)$ is clearly nonincreasing in $t$. $VC(0)=\infty$ (in fact, I think $VC(0.5)=\infty$).

Question: Have people studied this generalization of VC-dimension, or something similar? If so, what is it called, and can you point me to resources about it? What is the best lower bound on $VC(t)$? Thanks!

Edit: Here's the motivation for my question. For any set of non-coplanar $d+1$ points in $R^d$, and any binary labeling of these points, there is a linear classifier (a hyperplane) which classifies these points with 100% accuracy. More generally, given $n>d+1$ points (satisfying some "weak" condition like non-coplanarity) and a binary labeling of them, what can we say about the accuracy of a linear classifier for these points? If $t \in [0,1]$ and $VC(t) \geq n$, then we know that for any $n$ points in $R^d$ (satisfying some "weak" condition) and any labeling of these points, there is a linear classifier which has accuracy at least $t$ on these points.

  • $\begingroup$ The VC-dimension is defined on hypergraphs, and the geometric setting is just a highly structured special case. If you generalize the geometric setting as you are doing, you simply end up with the same old VC-dimension on another hypergraph. $\endgroup$
    – Gamow
    Mar 6, 2019 at 7:25
  • $\begingroup$ What is your motivation for this concept? Do you think it will lead to sharper generalization bounds? $\endgroup$
    – Aryeh
    Mar 6, 2019 at 8:24
  • $\begingroup$ @Aryeh - I added my motivation. I don't think it will lead to sharper generalization bounds $\endgroup$ Mar 7, 2019 at 4:14
  • $\begingroup$ So why is this notion an interesting one to explore then? $\endgroup$
    – Aryeh
    Mar 7, 2019 at 6:31
  • $\begingroup$ Thanks for sharing this post.It's useful for me. $\endgroup$
    – user52357
    Mar 22, 2019 at 11:52

1 Answer 1


If the $VC$-dimension is $d$, then the number of sets that can be cut out from $m$ points by your family is $(em/d)^d$. (This is called the shatter function.) Every set that you can cut out, can be changed at most $\sum_{i=0}^{tm} \binom mi$ ways to contribute to the $t$-shattering. So if you want to $t$-shatter a set of size $m$, then you need $2^m\le (em/d)^d \sum_{i=0}^{tm} \binom mi\le(em/d)^d 2^{H(t)m}$ (if $t\le 1/2$) where $H(t)=-t\log_2 t-(1-t)\log_2(1-t)$ is the binary entropy. From this we get $VC(t)\lessapprox d/(1-H(t))$. Also note that $VC(t)\ge d/(1-t)$ holds trivially. Of course, these bounds are general and not only for halfspaces.

  • $\begingroup$ Thanks for accepting, but I don't think I've really answered your question about halfspaces, I've only provided a lower and an upper bound. $\endgroup$
    – domotorp
    Mar 8, 2019 at 8:08

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