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Questions tagged [vc-dimension]

The VC dimension (for Vapnik–Chervonenkis dimension) is a measure of the capacity (complexity, expressive power, richness, or flexibility) of a statistical classification algorithm, defined as the cardinality of the largest set of points that the algorithm can shatter.

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I have this hypothesis class: H = {ha : R → {0, 1} | a > 0, a ∈ R, where ha(x) = 1−a,a = { 1, x ∈ [−a, a] 0, x ̸ ∈ [−a, a] } I need to compute the growth function for m>= 0. So I think that this ...
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This is usually shown by an application of the Statistical No Free Lunch Theorem. But is this possible to show this by working simply with the definition of PAC learnability and the sample complexity ...
JustBlaze's user avatar
1 vote
1 answer
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Statement : A half space is set of all points on one side of a linear separator, i.e., a set of the form $\{x \mid w^{T}x \ge t\}$. The VC-dimension of half spaces in $d$-dimensions is at least $d+1$. ...
Shi's user avatar
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VC dimension covers the binary classification case, i.e. when we want to get a predictor $X \to \{0, 1\}$. Using VC dimension, we can get the upper bound on the sample complexity for PAC-learning. In ...
Dmitry's user avatar
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Suppose a real-valued function class $\mathcal{F}$ with pseudo dimension less than $d$, I am wondering what is the pseudo dimension of the following function class \begin{equation} \mathcal{F}_2 = \{\...
Vassily's user avatar
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I am trying to determine some bounds for sample complexity. Suppose we have a bounded loss function $\ell$ and target function $f:\mathcal{X}\to\mathcal{Y}$. Hypothesis $h$ is learned, then the ...
somefellow's user avatar
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1 answer
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Let $H_{1}$ and $H_{2}$ are two hypothesis classes over some domain $X$1. If both $H_{1}$ and $H_{2}$ have the uniform convergence property, then do $H_{1}$ U $H_{2}$ have uniform convergence?
baxter8's user avatar
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Hello everyone I am new to the site, I have a question that was in the test and did not understand the parts that are in the question. This question from a test I failed to pass, in a machine learning ...
hah's user avatar
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2 votes
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I'm new here on the site, I'm a final year student in computer science. In a machine learning course, there was a question on a test that I could not understand. The question goes like this: Suppose ...
hah's user avatar
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1 vote
1 answer
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I still have some doubts for finding the VC-dimension. Suppose $\mathcal{H}$ has VC-dimension $n$. This is the process of how I think about it: (1) Show that there is a set of $n$ points that can be ...
M. Fire's user avatar
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1 vote
1 answer
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I have researched this topic in the last time, but no usefull results for me. So I'm here and I please you to help me with the following problem: What is $VCdim$($\mathcal{H}$), where $\mathcal{H}$ is ...
Math's user avatar
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What is the VC Dimension of the class of $k$-dimensional cross-polytope (1-norm ($l_1$) balls)? A $k$-dimensional $l_1$ ball with radius $r\in \mathcal R$ and center $\mathbb v\in \mathcal R^k$ is $\{\...
NineDarkOldAncestor's user avatar
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1 answer
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Let's say there are two sets of affine functions. $\mathcal{A} = \{ax +b \mid a,b \in \mathbb{R}\}$ $\mathcal{H} = \{2x + 1, x, 3x + 4, 4x\}$ I know that the $\mathrm{Pdim}(\mathcal{A}) = 2$. From ...
AB_IM's user avatar
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I have found this exercise and I cannot solve it: In $X=\mathbb R^2$, let's observe two models $H_1$ (rectangle with sides parallel to the coordinate axes) and $H_2$ (lines). We define a model $H_3$ ...
temp1231233232's user avatar
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This is a problem of VC that I've been trying to solve. Any help is appreciated. Let's assume hypothesis classes $H_{\mathit{init}}$ of initial segments over domain $X = \mathbb R$ and $H_{\mathit{...
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