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 67 articles , 1 standard article )

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  1. Jakubův, Jan; Urban, Josef: ENIGMA: efficient learning-based inference guiding machine (2017)
  2. Kotłowski, Wojciech; Dembczyński, Krzysztof: Surrogate regret bounds for generalized classification performance metrics (2017)
  3. 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)
  4. Rachkovskij, D.A.: Binary vectors for fast distance and similarity estimation (2017)
  5. Wu, Yu-Ping; Lin, Hsuan-Tien: Progressive random $k$-labelsets for cost-sensitive multi-label classification (2017)
  6. Yu, Fei; Zhang, Min-Ling: Maximum margin partial label learning (2017)
  7. Doğan, Ürün; Glasmachers, Tobias; Igel, Christian: A unified view on multi-class support vector classification (2016)
  8. Ganin, Yaroslav; Ustinova, Evgeniya; Ajakan, Hana; Germain, Pascal; Larochelle, Hugo; Laviolette, François; Marchand, Mario; Lempitsky, Victor: Domain-adversarial training of neural networks (2016)
  9. Maximilian Schmitt, Bjoern W. Schuller: openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit (2016) arXiv
  10. Park, Young Woong; Klabjan, Diego: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning (2016)
  11. 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)
  12. Tianqi Chen, Carlos Guestrin: XGBoost: A Scalable Tree Boosting System (2016) arXiv
  13. Treister, Eran; Turek, Javier S.; Yavneh, Irad: A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (2016)
  14. Van Esbroeck, Alex; Smith, Landon; Syed, Zeeshan; Singh, Satinder; Karam, Zahi: Multi-task seizure detection: addressing intra-patient variation in seizure morphologies (2016)
  15. Wiens, Jenna; Guttag, John; Horvitz, Eric: Patient risk stratification with time-varying parameters: A multitask learning approach (2016) ioport
  16. Zhang, Chong; Liu, Yufeng; Wu, Yichao: On quantile regression in reproducing kernel Hilbert spaces with the data sparsity constraint (2016)
  17. Zhou, Mingyuan; Cong, Yulai; Chen, Bo: Augmentable gamma belief networks (2016)
  18. Chen, Minmin; Weinberger, Kilian Q.; Xu, Zhixiang (Eddie); Sha, Fei: Marginalizing stacked linear denoising autoencoders (2015) ioport
  19. Do, Thanh-Nghi; Poulet, François: Parallel multiclass logistic regression for classifying large scale image datasets (2015) ioport
  20. Durrant, Robert J.; Kabán, Ata: Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions (2015)

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