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 154 articles , 1 standard article )

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  1. Adam Pocock: Tribuo: Machine Learning with Provenance in Java (2021) arXiv
  2. Blanchard, Gilles; Deshmukh, Aniket Anand; Dogan, Urun; Lee, Gyemin; Scott, Clayton: Domain generalization by marginal transfer learning (2021)
  3. Campigotto, Paolo; Teso, Stefano; Battiti, Roberto; Passerini, Andrea: Learning modulo theories for constructive preference elicitation (2021)
  4. Civitelli, Enrico; Lapucci, Matteo; Schoen, Fabio; Sortino, Alessio: An effective procedure for feature subset selection in logistic regression based on information criteria (2021)
  5. Cristofari, Andrea; Rinaldi, Francesco: A derivative-free method for structured optimization problems (2021)
  6. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  7. Li, Yanting; Jin, Junwei; Zhao, Liang; Wu, Huaiguang; Sun, Lijun; Philip Chen, C. L.: A neighborhood prior constrained collaborative representation for classification (2021)
  8. Mudunuru, M. K.; Karra, S.: Physics-informed machine learning models for predicting the progress of reactive-mixing (2021)
  9. Rezaei, Mostafa; Cribben, Ivor; Samorani, Michele: A clustering-based feature selection method for automatically generated relational attributes (2021)
  10. Sun, Yuan; Ernst, Andreas; Li, Xiaodong; Weiner, Jake: Generalization of machine learning for problem reduction: a case study on travelling salesman problems (2021)
  11. Żelasko, Dariusz; Pławiak, Paweł: Ensemble learning techniques for transmission quality classification in a pay&require multi-layer network (2021)
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  13. Bertsimas, Dimitris; Pauphilet, Jean; van Parys, Bart: Sparse regression: scalable algorithms and empirical performance (2020)
  14. Gauthier, Thibault: Tree neural networks in HOL4 (2020)
  15. Khandagale, Sujay; Xiao, Han; Babbar, Rohit: Bonsai: diverse and shallow trees for extreme multi-label classification (2020)
  16. Lee, Taito; Matsushima, Shin; Yamanishi, Kenji: Grafting for combinatorial binary model using frequent itemset mining (2020)
  17. Massias, Mathurin; Vaiter, Samuel; Gramfort, Alexandre; Salmon, Joseph: Dual extrapolation for sparse GLMs (2020)
  18. Pensar, Johan; Xu, Yingying; Puranen, Santeri; Pesonen, Maiju; Kabashima, Yoshiyuki; Corander, Jukka: High-dimensional structure learning of binary pairwise Markov networks: a comparative numerical study (2020)
  19. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  20. Vanzo, Andrea; Croce, Danilo; Bastianelli, Emanuele; Basili, Roberto; Nardi, Daniele: Grounded language interpretation of robotic commands through structured learning (2020)

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