LiblineaR.ACF: Linear Classification with Online Adaptation of Coordinate Frequencies

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 (<https://cran.r-project.org/package=LiblineaR>). 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.

Install the latest version of this package by entering the following in R:
install.packages("LiblineaR.ACF")
AuthorAydin Demircioglu <aydin.demircioglu@ini.rub.de>; Tobias Glasmachers <tobias.glasmachers@ini.rub.de>; Urun Dogan <urundogan@gmail.com>
Date of publication2016-01-04 12:39:03
MaintainerAydin Demircioglu <aydin.demircioglu@ini.rub.de>
LicenseGPL-2
Version1.94-2
http://github.com/aydindemircioglu/liblineaR.ACF/

View on CRAN

Files

inst
inst/CITATION
tests
tests/testthat.R
tests/testthat
tests/testthat/test_Liblinear.R
src
src/Makevars
src/trainLinear.c
src/tron.h
src/tron.cpp
src/linear.h
src/predictLinear.c
src/linear.cpp
NAMESPACE
R
R/LiblineaR.ACF.R R/predict.R R/zzz.R
MD5
DESCRIPTION
man
man/LiblineaR.ACF.Rd man/predict.LiblineaR.ACF.Rd
cleanup

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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