An Improved Bound on the VC-Dimension of Neural Networks with Polynomial Activation Functions
/ Authors
/ 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.
Journal: arXiv: Optimization and Control