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An Improved Bound on the VC-Dimension of Neural Networks with Polynomial Activation Functions

J. Maurice Rojas·M. Vidyasagar
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Abstract

In this note, we derive an improved upper bound for the VC-dimension of neural networks with polynomial activation functions. This improved bound is based on a result of Rojas on the number of connected components of a semi-algebraic set.

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J. Maurice Rojas, M. Vidyasagar. 2002-02-01. An Improved Bound on the VC-Dimension of Neural Networks with Polynomial Activation Functions. https://arxiv.org/abs/math/0112208

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