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Neural Computation, Vol 9, 765-769, Copyright © 1997 by The MIT Press


NOTES

Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'

Wee Sun Lee, Peter L. Bartlett and Robert C. Williamson

The earlier article gives lower bounds on the VC-dimension of various smoothly parameterized function classes. The results were proved by showing a relationship between the uniqueness of decision boundaries and the VC-dimension of smoothly parameterized function classes. The proof is incorrect; there is no such relationship under the conditions stated in the article. For the case of neural networks with tanh activation functions, wegive an alternative proof of a lower bound for the VC-dimension proportional to the number of parameters, which holds even when the magnitude of the parameters is restricted to be arbitrarily small.


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M. Schmitt
Neural Networks with Local Receptive Fields and Superlinear VC Dimension
Neural Comput., April 1, 2002; 14(4): 919 - 956.
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Copyright © 1997 by The MIT Press.