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Dan Chazan
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Publisher: Journals Gateway
Neural Computation (1997) 9 (4): 771–776.
Published: 15 May 1997
Abstract
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We show that the VC-dimension of a smoothly parameterized function class is not less than the dimension of any manifold in the parameter space, as long as distinct parameter values induce distinct decision boundaries. A similar theorem was published recently and used to introduce lower bounds on VC-dimension for several cases (Lee, Bartlett, & Williamson, 1995). This theorem is not correct, but our theorem could replace it for those cases and many other practical ones.