LiblineaR (2.10-8)

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Linear Predictive Models Based on the 'LIBLINEAR' C/C++ Library.

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.

Maintainer: Thibault Helleputte
Author(s): Thibault Helleputte <>; Pierre Gramme <>; Jerome Paul <>

License: GPL-2

Uses: SparseM
Reverse depends: LinearizedSVR
Reverse suggests: bigstatsr, mlr, RSSL

Released almost 3 years ago.

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