  We formulate the Multiple Kernel Learning (abbreviated as MKL) problem for
the support vector machine with the infamous $(0,1)$-loss function. Some
first-order optimality conditions are given and then exploited to develop a
fast ADMM solver for the nonconvex and nonsmooth optimization problem. A simple
numerical experiment on synthetic planar data shows that our MKL-$L_{0/1}$-SVM
framework could be promising.
