LiblineaR (version 2.10-8)
Linear Predictive Models Based on the 'LIBLINEAR' C/C++ Library
Description
A wrapper around the 'LIBLINEAR' C/C++ library for machine
learning (available at
). '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.