1 research outputs found
A Smoothing Algorithm for l1 Support Vector Machines
A smoothing 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 datasets. The algorithm uses
smoothing for the hinge-loss function, and an active set approach for the
penalty. The smoothing parameter is initially large, but
typically halved when the smoothed problem is solved to sufficient accuracy.
Convergence theory is presented that shows
guarded Newton steps for each value
of except for asymptotic bands and
, with only one Newton step provided ,
where is the number of data points and the stopping criterion that the
predicted reduction is less than . The experimental results show
that our algorithm is capable of strong test accuracy without sacrificing
training speed.Comment: arXiv admin note: text overlap with arXiv:1808.0710