Cross-Validation, Bootstrap, and Support Vector Machines

Abstract EN

This paper considers the applications of resampling methods to support vector machines (SVMs).

We take into account the leaving-one-out cross-validation (CV) when determining the optimum tuning parameters and bootstrapping the deviance in order to summarize the measure of goodness-of-fit in SVMs.

The leaving-one-out CV is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples.

We analyze the data from a mackerel-egg survey and a liver-disease study.