LIBLINEAR

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 90 articles , 1 standard article )

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  1. Aggarwal, Charu C.: Machine learning for text (2018)
  2. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  3. Fawzi, Alhussein; Fawzi, Omar; Frossard, Pascal: Analysis of classifiers’ robustness to adversarial perturbations (2018)
  4. Freitag, Michael; Amiriparian, Shahin; Pugachevskiy, Sergey; Cummins, Nicholas; Schuller, Björn: auDeep: unsupervised learning of representations from audio with deep recurrent neural networks (2018)
  5. Fu, Sheng; Zhang, Sanguo; Liu, Yufeng: Adaptively weighted large-margin angle-based classifiers (2018)
  6. Horn, Daniel; Demircioğlu, Aydın; Bischl, Bernd; Glasmachers, Tobias; Weihs, Claus: A comparative study on large scale kernelized support vector machines (2018)
  7. Ingimundardottir, Helga; Runarsson, Thomas Philip: Discovering dispatching rules from data using imitation learning: a case study for the job-shop problem (2018)
  8. Lausser, Ludwig; Schmid, Florian; Schirra, Lyn-Rouven; Wilhelm, Adalbert F. X.; Kestler, Hans A.: Rank-based classifiers for extremely high-dimensional gene expression data (2018)
  9. Liu, Dalian; Li, Dewei; Shi, Yong; Tian, Yingjie: Large-scale linear nonparallel SVMs (2018)
  10. Manno, Andrea; Palagi, Laura; Sagratella, Simone: Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training (2018)
  11. Piccialli, Veronica; Sciandrone, Marco: Nonlinear optimization and support vector machines (2018)
  12. Roumani, Yazan F.; Roumani, Yaman; Nwankpa, Joseph K.; Tanniru, Mohan: Classifying readmissions to a cardiac intensive care unit (2018)
  13. Smith, Virginia; Forte, Simone; Ma, Chenxin; Takáč, Martin; Jordan, Michael I.; Jaggi, Martin: CoCoA: a general framework for communication-efficient distributed optimization (2018)
  14. 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)
  15. Zhang, Quan; Zhou, Mingyuan: Permuted and augmented stick-breaking Bayesian multinomial regression (2018)
  16. Zhou, Mingyuan: Nonparametric Bayesian negative binomial factor analysis (2018)
  17. Jakubův, Jan; Urban, Josef: ENIGMA: efficient learning-based inference guiding machine (2017)
  18. Kotłowski, Wojciech; Dembczyński, Krzysztof: Surrogate regret bounds for generalized classification performance metrics (2017)
  19. 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)
  20. Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Salmon, Joseph: Gap safe screening rules for sparsity enforcing penalties (2017)

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