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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

May 15, 1997, Vol. 9, No. 4, Pages 765-769
(doi: 10.1162/neco.1997.9.4.765)
© 1997 Massachusetts Institute of Technology
Correction to “Lower Bounds on VC-Dimension of Smoothly Parameterized Function Classes”1
Article PDF (70.85 KB)
Abstract

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, we give 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.