Smoothed Hinge Loss and $\ell^{1}$ Support Vector Machines
Optimization and Control
2018-08-23 v1 Numerical Analysis
Abstract
A new algorithm is presented for solving the soft-margin Support Vector Machine (SVM) optimization problem with an 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 penalty.
Keywords
Cite
@article{arxiv.1808.07100,
title = {Smoothed Hinge Loss and $\ell^{1}$ Support Vector Machines},
author = {Jeffrey Hajewski and Suely Oliveira and David E. Stewart},
journal= {arXiv preprint arXiv:1808.07100},
year = {2018}
}
Comments
13 pp, 1 figure