An ADMM Solver for the MKL-$L_{0/1}$-SVM
2023
Online
Elektronische Ressource
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.
Comment: 8 pages, 3 figures, 2 tables. Submitted to the 62nd IEEE Conference on Decision and Control as a Regular paper, with a shortened version (arXiv version 1) submitted to the 3rd Chinese Conference on Predictive Control and Intelligent Decision (CPCID) as an Extended Abstract
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An ADMM Solver for the MKL-$L_{0/1}$-SVM
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Veröffentlichung: | 2023 |
Medientyp: | Elektronische Ressource |
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