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 79 articles , 1 standard article )

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  1. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  2. Fawzi, Alhussein; Fawzi, Omar; Frossard, Pascal: Analysis of classifiers’ robustness to adversarial perturbations (2018)
  3. Fu, Sheng; Zhang, Sanguo; Liu, Yufeng: Adaptively weighted large-margin angle-based classifiers (2018)
  4. Manno, Andrea; Palagi, Laura; Sagratella, Simone: Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training (2018)
  5. Piccialli, Veronica; Sciandrone, Marco: Nonlinear optimization and support vector machines (2018)
  6. Roumani, Yazan F.; Roumani, Yaman; Nwankpa, Joseph K.; Tanniru, Mohan: Classifying readmissions to a cardiac intensive care unit (2018)
  7. Xie, Hao; Du, Yunyan; Yu, Huapeng; Chang, Yongxin; Xu, Zhiyong; Tang, Yuanyan: Open set face recognition with deep transfer learning and extreme value statistics (2018)
  8. Jakubův, Jan; Urban, Josef: ENIGMA: efficient learning-based inference guiding machine (2017)
  9. Kotłowski, Wojciech; Dembczyński, Krzysztof: Surrogate regret bounds for generalized classification performance metrics (2017)
  10. Lan, Liang; Zhang, Kai; Ge, Hancheng; Cheng, Wei; Liu, Jun; Rauber, Andreas; Li, Xiao-Li; Wang, Jun; Zha, Hongyuan: Low-rank decomposition meets kernel learning: a generalized Nyström method (2017)
  11. Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Salmon, Joseph: Gap safe screening rules for sparsity enforcing penalties (2017)
  12. Rachkovskij, D. A.: Binary vectors for fast distance and similarity estimation (2017)
  13. Roy, Asis; Bhattacharya, Sourangshu; Guin, Kalyan: Prediction of esophageal cancer using demographic, lifestyle, patient history, and basic clinical tests (2017)
  14. Schmitt, Maximilian; Schuller, Björn: openXBOW -- introducing the Passau open-source crossmodal bag-of-words toolkit (2017)
  15. Wu, Yu-Ping; Lin, Hsuan-Tien: Progressive random $k$-labelsets for cost-sensitive multi-label classification (2017)
  16. Yu, Fei; Zhang, Min-Ling: Maximum margin partial label learning (2017)
  17. Doğan, Ürün; Glasmachers, Tobias; Igel, Christian: A unified view on multi-class support vector classification (2016)
  18. Ganin, Yaroslav; Ustinova, Evgeniya; Ajakan, Hana; Germain, Pascal; Larochelle, Hugo; Laviolette, François; Marchand, Mario; Lempitsky, Victor: Domain-adversarial training of neural networks (2016)
  19. Maximilian Schmitt, Bjoern W. Schuller: openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit (2016) arXiv
  20. Park, Young Woong; Klabjan, Diego: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning (2016)

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