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.

翻译：我们针对带有著名的$(0,1)$损失函数的支持向量机，提出了多核学习（简称MKL）问题的公式化表述。给出了一些一阶最优性条件，并利用这些条件开发了一种快速的ADMM求解器，用于求解该非凸非光滑优化问题。在合成平面数据上的简单数值实验表明，我们的MKL-$L_{0/1}$-SVM框架颇具潜力。