On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2013.
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2 papers.