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 74 articles , 1 standard article )

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  9. Schmitt, Maximilian; Schuller, Björn: openXBOW -- introducing the Passau open-source crossmodal bag-of-words toolkit (2017)
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  11. Yu, Fei; Zhang, Min-Ling: Maximum margin partial label learning (2017)
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  15. Park, Young Woong; Klabjan, Diego: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning (2016)
  16. Souillard-Mandar, William; Davis, Randall; Rudin, Cynthia; Au, Rhoda; Libon, David J.; Swenson, Rodney; Price, Catherine C.; Lamar, Melissa; Penney, Dana L.: Learning classification models of cognitive conditions from subtle behaviors in the digital clock drawing test (2016)
  17. Tianqi Chen, Carlos Guestrin: XGBoost: A Scalable Tree Boosting System (2016) arXiv
  18. Treister, Eran; Turek, Javier S.; Yavneh, Irad: A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (2016)
  19. Van Esbroeck, Alex; Smith, Landon; Syed, Zeeshan; Singh, Satinder; Karam, Zahi: Multi-task seizure detection: addressing intra-patient variation in seizure morphologies (2016)
  20. Wiens, Jenna; Guttag, John; Horvitz, Eric: Patient risk stratification with time-varying parameters: A multitask learning approach (2016) ioport

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