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

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.

13 pp, 1 figure

References in corpus (1)

• Scikit-learn: Machine Learning in Python