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

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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.

Author
Aydin Demircioglu <aydin.demircioglu@ini.rub.de>; Tobias Glasmachers <tobias.glasmachers@ini.rub.de>; Urun Dogan <urundogan@gmail.com>
Date of publication
2016-01-04 12:39:03
Maintainer
Aydin Demircioglu <aydin.demircioglu@ini.rub.de>
License
GPL-2
Version
1.94-2
URLs

View on CRAN

Man pages

LiblineaR.ACF
Linear predictive models estimation with Online Adaptation of...
predict.LiblineaR.ACF
Predictions with LiblineaR.ACF model

Files in this package

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