  This paper presents a Multiple Kernel Learning (abbreviated as MKL) framework
for the Support Vector Machine (SVM) with the $(0, 1)$ loss function. Some
KKT-like first-order optimality conditions are provided and then exploited to
develop a fast ADMM algorithm to solve the nonsmooth nonconvex optimization
problem. Numerical experiments on real data sets show that the performance of
our MKL-$L_{0/1}$-SVM is comparable with the one of the leading approaches
called SimpleMKL developed by Rakotomamonjy, Bach, Canu, and Grandvalet
[Journal of Machine Learning Research, vol. 9, pp. 2491-2521, 2008].
