Smoothed Hinge Loss and $\ell^{1}$ Support Vector Machines

Jeffrey Hajewski; Suely Oliveira; David E. Stewart

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arXiv · 1808.07100

Smoothed Hinge Loss and $\ell^{1}$ Support Vector Machines

Jeffrey Hajewski·Suely Oliveira·David E. Stewart

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Abstract

A new algorithm is presented for solving the soft-margin Support Vector Machine (SVM) optimization problem with an $\ell^{1}$ penalty. This algorithm is designed to require a modest number of passes over the data, which is an important measure of its cost for very large data sets. The algorithm uses smoothing for the hinge-loss function, and an active set approach for the $\ell^{1}$ penalty.

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BibTeX RIS

Jeffrey Hajewski, Suely Oliveira, David E. Stewart. 2018-08-21. Smoothed Hinge Loss and $\ell^{1}$ Support Vector Machines. https://arxiv.org/abs/1808.07100

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