LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets.

References in zbMATH (referenced in 53 articles , 1 standard article )

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  1. Doğan, Ürün; Glasmachers, Tobias; Igel, Christian: A unified view on multi-class support vector classification (2016)
  2. Ganin, Yaroslav; Ustinova, Evgeniya; Ajakan, Hana; Germain, Pascal; Larochelle, Hugo; Laviolette, François; Marchand, Mario; Lempitsky, Victor: Domain-adversarial training of neural networks (2016)
  3. Treister, Eran; Turek, Javier S.; Yavneh, Irad: A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (2016)
  4. Wiens, Jenna; Guttag, John; Horvitz, Eric: Patient risk stratification with time-varying parameters: A multitask learning approach (2016)
  5. Zhang, Chong; Liu, Yufeng; Wu, Yichao: On quantile regression in reproducing kernel Hilbert spaces with the data sparsity constraint (2016)
  6. Zhou, Mingyuan; Cong, Yulai; Chen, Bo: Augmentable gamma belief networks (2016)
  7. Chen, Minmin; Weinberger, Kilian Q.; Xu, Zhixiang (Eddie); Sha, Fei: Marginalizing stacked linear denoising autoencoders (2015)
  8. Do, Thanh-Nghi; Poulet, François: Parallel multiclass logistic regression for classifying large scale image datasets (2015)
  9. Durrant, Robert J.; Kabán, Ata: Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions (2015)
  10. Fernandez-Lozano, Carlos; Cuiñas, Rubén F.; Seoane, José A.; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R.: Classification of signaling proteins based on molecular star graph descriptors using machine learning models (2015)
  11. Letham, Benjamin; Rudin, Cynthia; McCormick, Tyler H.; Madigan, David: Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model (2015)
  12. Xu, Yangyang; Yin, Wotao: Block stochastic gradient iteration for convex and nonconvex optimization (2015)
  13. Yadav, Arvind R.; Anand, R.S.; Dewal, M.L.; Gupta, Sangeeta: Hardwood species classification with DWT based hybrid texture feature extraction techniques (2015)
  14. Claesen, Marc; De Smet, Frank; Suykens, Johan A.K.; De Moor, Bart: EnsembleSVM: a library for ensemble learning using support vector machines (2014)
  15. Dong, Yuan; Gao, Shan; Tao, Kun; Liu, Jiqing; Wang, Haila: Performance evaluation of early and late fusion methods for generic semantics indexing (2014)
  16. Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Amorim, Dinani: Do we need hundreds of classifiers to solve real world classification problems? (2014)
  17. Kobayashi, Takumi: Low-rank bilinear classification: efficient convex optimization and extensions (2014)
  18. Liu, Kun; Skibbe, Henrik; Schmidt, Thorsten; Blein, Thomas; Palme, Klaus; Brox, Thomas; Ronneberger, Olaf: Rotation-invariant HOG descriptors using Fourier analysis in polar and spherical coordinates (2014)
  19. Maratea, Antonio; Petrosino, Alfredo; Manzo, Mario: Adjusted F-measure and kernel scaling for imbalanced data learning (2014)
  20. Mesnil, Grégoire; Bordes, Antoine; Weston, Jason; Chechik, Gal; Bengio, Yoshua: Learning semantic representations of objects and their parts (2014)

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