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 160 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. Borboudakis, Giorgos; Tsamardinos, Ioannis: Extending greedy feature selection algorithms to multiple solutions (2021)
  4. Campigotto, Paolo; Teso, Stefano; Battiti, Roberto; Passerini, Andrea: Learning modulo theories for constructive preference elicitation (2021)
  5. Chen, Junyang; Gong, Zhiguo; Wang, Wei; Liu, Weiwen: HNS: hierarchical negative sampling for network representation learning (2021)
  6. Civitelli, Enrico; Lapucci, Matteo; Schoen, Fabio; Sortino, Alessio: An effective procedure for feature subset selection in logistic regression based on information criteria (2021)
  7. Cristofari, Andrea; Rinaldi, Francesco: A derivative-free method for structured optimization problems (2021)
  8. Duan, Zhen; Sun, Xian; Zhao, Shu; Chen, Jie; Zhang, Yanping; Tang, Jie: Hierarchical community structure preserving approach for network embedding (2021)
  9. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  10. Leleux, Pierre; Courtain, Sylvain; Guex, Guillaume; Saerens, Marco: Sparse randomized shortest paths routing with Tsallis divergence regularization (2021)
  11. Li, Yanting; Jin, Junwei; Zhao, Liang; Wu, Huaiguang; Sun, Lijun; Philip Chen, C. L.: A neighborhood prior constrained collaborative representation for classification (2021)
  12. Mortier, Thomas; Wydmuch, Marek; Dembczyński, Krzysztof; Hüllermeier, Eyke; Waegeman, Willem: Efficient set-valued prediction in multi-class classification (2021)
  13. Mudunuru, M. K.; Karra, S.: Physics-informed machine learning models for predicting the progress of reactive-mixing (2021)
  14. Rezaei, Mostafa; Cribben, Ivor; Samorani, Michele: A clustering-based feature selection method for automatically generated relational attributes (2021)
  15. Stankova, Marija; Praet, Stiene; Martens, David; Provost, Foster: Node classification over bipartite graphs through projection (2021)
  16. Sun, Yuan; Ernst, Andreas; Li, Xiaodong; Weiner, Jake: Generalization of machine learning for problem reduction: a case study on travelling salesman problems (2021)
  17. Yoshida, Tomoki; Takeuchi, Ichiro; Karasuyama, Masayuki: Distance metric learning for graph structured data (2021)
  18. Żelasko, Dariusz; Pławiak, Paweł: Ensemble learning techniques for transmission quality classification in a pay&require multi-layer network (2021)
  19. Zhou, Shenglong; Pan, Lili; Xiu, Naihua; Qi, Hou-Duo: Quadratic convergence of smoothing Newton’s method for 0/1 loss optimization (2021)
  20. Aggarwal, Charu C.: Linear algebra and optimization for machine learning. A textbook (2020)

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