R package LiblineaR: 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.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Yu, Weichang; Ormerod, John T.; Stewart, Michael: Variational discriminant analysis with variable selection (2020)
- Murai, Fabricio; Rennó, Diogo; Ribeiro, Bruno; Pappa, Gisele L.; Towsley, Don; Gile, Krista: Selective harvesting over networks (2018)
- Zhang, Quan; Zhou, Mingyuan: Permuted and augmented stick-breaking Bayesian multinomial regression (2018)
- Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)