MKL-$L_{0/1}$-SVM
This work addresses kernel selection in SVMs for machine learning practitioners, but it is incremental as it builds on existing MKL methods with a different loss function.
The paper tackles the problem of multiple kernel learning for SVMs with a nonconvex (0,1) loss function by developing a fast ADMM algorithm, achieving performance comparable to the leading SimpleMKL approach on real datasets.
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].