APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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In VC dimensions of neural networks what does VC stand for?
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Vladimir Cherubim
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Vapnik Chervonenkis
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Victor Charlie
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Vanessa Carlton
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Explanation:
Detailed explanation-1: -In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the capacity (complexity, expressive power, richness, or flexibility) of a set of functions that can be learned by a statistical binary classification algorithm.
Detailed explanation-2: -Definition 3.3 (VC dimension). The VC dimension of a hypothesis class, VC-dim(H), is defined as the maximal cardinality of a finite set A that is shattered.
Detailed explanation-3: -"The VC Dimension of affine classifiers of the form f(x)=w⋅x+b in n dimensions − i.e. w∈Rn − is n+1": this corresponds to the case of what is called a linear SVM. “The VC Dimension of an SVM equipped with an RBF kernel is infinite."
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