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Calculate the VC dimension site:www.quora.com

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The VC dimension of a class H is defined as the size of the largest set A that H can shatter. H shatters a set A if for every subset B of A, there exists an ...

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Finally, we say the VC dimension of the hypothesis class is , if the highest cardinality (i.e. the number of points) of dataset which can shatter is .

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The class of functions computed by multilayer neural networks with binary as well as linear activations and ρ weights has VC dimension O(ρ^2).

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VC dimension is the measure of how complex a classifier. ... -1,….,+1 tuples (there are 2^N of them if you count) can be hypothesized by the learning model.

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You can define an analogue of VC dimension for real-valued functions and use that to get generalization bounds on regressors.

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The Rademacher complexity provides similar bounds to the VC, and can sometimes provide more insight than VC dimension calculations into such statistical methods ...

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The Rademacher complexity is distribution-dependent. That makes it harder to calculate and work with, but it means that you can get better bounds for your ...

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Explanation for shattering is given here: VC dimension The break-point is nothing but VC dimension + 1. So basically, what you are trying to do is find out ...

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The Rademacher complexity provides similar bounds to the VC, and can sometimes provide more insight than VC dimension calculations into such statistical ...

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VC Dimension is defined as the cardinality of the largest set of points that the ... How is VC-dimension calculated and what is its use in machine learning?