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.
|Author||Thibault Helleputte <[email protected]>; Pierre Gramme <[email protected]>; Jerome Paul <[email protected]>|
|Maintainer||Thibault Helleputte <[email protected]>|
|Package repository||View on CRAN|
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