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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.

On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods