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Old 02-21-2013, 06:23 PM
ilya239 ilya239 is offline
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Join Date: Jul 2012
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Question SVMs and the input distribution

If the input distribution has high density near the target boundary, the sample will likely contain points near the boundary, so that large-margin or small-margin classifiers will be similar. If the input distribution has low density near the boundary, then the sample will have few near-boundary points, giving advantage to a large-margin classifier -- but then also, the probability of drawing a near-margin point during out-of-sample use is low, so E_out for low-margin classifiers is not much affected.

Why does this not limit the advantage of large-margin classifiers in practice?
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