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 synthetic and real datasets 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].

翻译：本文提出了一种适用于支持向量机（SVM）且带有$(0, 1)$损失函数的多核学习框架。文中给出了类似KKT条件的一阶最优性条件，并利用这些条件开发了一种快速的ADMM算法来求解该非光滑非凸优化问题。在合成数据集和真实数据集上的数值实验表明，本文提出的MKL-$L_{0/1}$-SVM的性能与Rakotomamonjy、Bach、Canu和Grandvalet提出的领先方法SimpleMKL相媲美（详见《机器学习研究期刊》第9卷，第2491-2521页，2008年）。