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On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods
Published 29 Jan 2013 in stat.ML and cs.LG | (1301.6944v1)
Abstract: It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.
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