Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces. (arXiv:2112.12961v2 [stat.ML] UPDATED)

Support vector machine (SVM) is a powerful classification method that has
achieved great success in many fields. Since its performance can be seriously
impaired by redundant covariates, model selection techniques are widely used
for SVM with high dimensional covariates. As an alternative to model selection,
significant progress has been made in the area of model averaging in the past
decades. Yet no frequentist model averaging method was considered for SVM. This
work aims to fill the gap and to propose …

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