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

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

2001-12-19

arxiv.org/abs/math/0112208v3

Mathematics

Optimization and Control

9 pages, submitted for publication. Various typos fixed and the proof of the main result has been streamlined

Scientific paper

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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