LiblineaR v2.10-8

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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 <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.

Functions in LiblineaR

Name Description
heuristicC Fast Heuristics For The Estimation Of the C Constant Of A Support Vector Machine.
predict.LiblineaR Predictions with LiblineaR model
LiblineaR Linear predictive models estimation based on the 'LIBLINEAR' C/C++ Library.
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Encoding UTF-8
Date 2017-02-13
License GPL-2
LazyLoad yes
URL http://dnalytics.com/liblinear/
RoxygenNote 5.0.1
NeedsCompilation yes
Packaged 2017-02-13 09:17:41 UTC; jey
Repository CRAN
Date/Publication 2017-02-13 12:58:40

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