Description: A wrapper around the LIBLINEAR C/C++ library for machine
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
Author: Thibault Helleputte [cre, aut, cph],

Title: Linear Predictive Models Based on the 'LIBLINEAR' C/C++ Library
Author: Thibault Helleputte <thibault.helleputte@dnalytics.com>; Pierre
Description: A wrapper around the 'LIBLINEAR' C/C++ library for machine
a simple library for solving large-scale regularized linear

Title: Linear Classification with Online Adaptation of Coordinate
Description: Solving the linear SVM problem with coordinate descent
is very efficient and is implemented in one of the most often used packages,
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
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
Copyright: The LIBLINEAR Project; Thibault Helleputte
Packaged: 2016-01-04 11:27:37 UTC; drunkeneye

Title: Linearized Bregman Algorithms for Generalized Linear Models
Author: Feng Ruan, Jiechao Xiong and Yuan Yao
SystemRequirements: GNU Scientific Library (GSL)
Description: Efficient procedures for fitting the lasso regularization path for linear regression, logistic and multinomial regression. The package uses Linearized Bregman Algorithm to solve the regularization path through iterations.