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Liblinear
Jul 20, 2023
Library for Large Linear Classification
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports
- L2-regularized classifiers
- L2-loss linear SVM, L1-loss linear SVM, and logistic regression LR
- L1-regularized classifiers after version 1.4
- L2-loss linear SVM and logistic regression LR
- L2-regularized support vector regression after version 1.9
- L2-loss linear SVR and L1-loss linear SVR.
Main features of LIBLINEAR include
- Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
- Multi-class classification 1 one-vs-the rest, 2 Crammer & Singer
- Cross validation for model evaulation
- Automatic parameter selection
- Probability estimates logistic regression only
- Weights for unbalanced data
- MATLAB/Octave, Java, Python, Ruby interfaces
Checkout these related ports:
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- Zotero - Reference management for bibliographic data and research materials
- Yoda - Particle physics package with classes for data analysis, histogramming
- Xtb - Semiempirical Extended Tight-Binding Program Package
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- Xcrysden - Crystalline and molecular structure visualisation program
- Xcfun - Exchange-correlation functionals with arbitrary-order derivatives
- Wxmacmolplt - Graphical user interface principally for the GAMESS program
- Wwplot - Plotting tool for experimental physics classes
- Wannier90 - Maximally-localized Wannier functions (MLWFs) and Wannier90
- Votca - CSG and XTP libraries for atomistic simulations
- Voro++ - Three-dimensional computations of the Voronoi tessellation
- Vmd - Molecular visualization program
- Vipster - Crystalline and molecular structure visualisation program