LiblineaR.ACF: Linear Classification with Online Adaptation of Coordinate Frequencies
Version 1.94-2

Solving the linear SVM problem with coordinate descent is very efficient and is implemented in one of the most often used packages, 'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear). It has been shown that the uniform selection of coordinates can be accelerated by using an online adaptation of coordinate frequencies (ACF). This package implements ACF and is based on 'LIBLINEAR' as well as the 'LiblineaR' package (). It currently supports L2-regularized L1-loss as well as L2-loss linear SVM. Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and Crammer & Singer method) and cross validation for model selection is supported. The training of the models based on ACF is much faster than standard 'LIBLINEAR' on many problems.

Getting started

Package details

AuthorAydin Demircioglu <[email protected]>; Tobias Glasmachers <[email protected]>; Urun Dogan <[email protected]>
Date of publication2016-01-04 12:39:03
MaintainerAydin Demircioglu <[email protected]>
LicenseGPL-2
Version1.94-2
URL http://github.com/aydindemircioglu/liblineaR.ACF/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("LiblineaR.ACF")

Try the LiblineaR.ACF package in your browser

Any scripts or data that you put into this service are public.

LiblineaR.ACF documentation built on May 29, 2017, 6:06 p.m.