mlsanityData to 7 Oct 2026

An ADMM Solver for the MKL-L0/1-SVM

arXiv 2303.04445 ↗First posted 8 Mar 2023stat.ML cs.LGcited by 1

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Abstract (arXiv)

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-L0/1-SVM framework could be promising.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 1 of its 18 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

1 of 18 references.

References · 1

  1. 2014On the Global Linear Convergence of the ADMM with Multi-Block Variablescited by 6

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023MKL-L0/1-SVMcited by 0