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arXiv · 2303.04445

An ADMM Solver for the MKL-$L_{0/1}$-SVM

Yijie Shi·Bin Zhu
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Abstract

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.

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Yijie Shi, Bin Zhu. 2023-03-31. An ADMM Solver for the MKL-$L_{0/1}$-SVM. https://arxiv.org/abs/2303.04445

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