Package: LiblineaR
Title: Linear Predictive Models Based on the LIBLINEAR C/C++ Library
Version: 1.94-2
Author: Thibault Helleputte <thibault.helleputte@dnalytics.com>; Pierre Gramme
<pierre.gramme@dnalytics.com>
Maintainer: Thibault Helleputte <thibault.helleputte@dnalytics.com>
Description: A wrapper around the LIBLINEAR C/C++ library for
machine learning (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
LIBLINEAR is a simple library for solving large-scale regularized linear
classification and regression. It currently supports L2-regularized
classification (such as logistic regression, L2-loss linear SVM and L1-loss
linear SVM) as well as L1-regularized classification (such as L2-loss linear
SVM and logistic regression) and L2-regularized support vector regression
(with L1- or L2-loss). The main features of LiblineaR include multi-class
classification (one-vs-the rest, and Crammer & Singer method), cross
validation for model selection, probability estimates (logistic regression
only) or weights for unbalanced data. The estimation of the models is
particularly fast as compared to other libraries.
License: GPL-2
Date: 2015-01-30
LazyLoad: yes
Suggests: SparseM
URL: http://dnalytics.com/liblinear/
Packaged: 2015-02-04 06:31:47 UTC; thibault
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-02-04 08:28:04
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