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Paper:

TR94-024 | 12th December 1994 00:00

Polynomial Bounds for VC Dimension of Sigmoidal Neural Networks

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TR94-024
Authors: Marek Karpinski, Angus Macintyre
Publication: 12th December 1994 00:00
Downloads: 1195
Keywords: 


Abstract:

We introduce a new method for proving explicit upper bounds on the VC
Dimension of general functional basis networks, and prove as an
application, for the first time, the VC Dimension of analog neural
networks with the sigmoid activation function $\sigma(y)=1/1+e^{-y}$
to be bounded by a quadratic polynomial in the number of programmable
parameters.



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