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In this paper, we introduce the discretized-Vapnik-Chervonenkis (VC) dimension for studying the complexity of a real function class, and then analyze properties of real function classes and neural networks. We first prove that a countable traversal set is enough to achieve the VC dimension for a real function class, whereas its classical definition states that the traversal set is the output range...
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