LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail:

References in zbMATH (referenced in 962 articles )

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  1. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (2020)
  2. Zhang, Yangjing; Zhang, Ning; Sun, Defeng; Toh, Kim-Chuan: An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems (2020)
  3. Abdulhussain, Sadiq H.; Ramli, Abd Rahman; Mahmmod, Basheera M.; Saripan, M. Iqbal; Al-Haddad, S. A. R.; Jassim, Wissam A.: A new hybrid form of Krawtchouk and Tchebichef polynomials: design and application (2019)
  4. Ahookhosh, Masoud; Neumaier, Arnold: An optimal subgradient algorithm with subspace search for costly convex optimization problems (2019)
  5. Alves, M. Marques; Geremia, Marina: Iteration complexity of an inexact Douglas-Rachford method and of a Douglas-Rachford-Tseng’s F-B four-operator splitting method for solving monotone inclusions (2019)
  6. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  7. Baumann, P.; Hochbaum, D. S.; Yang, Y. T.: A comparative study of the leading machine learning techniques and two new optimization algorithms (2019)
  8. Chen, Mingshuai; Wang, Jian; An, Jie; Zhan, Bohua; Kapur, Deepak; Zhan, Naijun: NIL: learning nonlinear interpolants (2019)
  9. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  10. Cristofari, Andrea: An almost cyclic 2-coordinate descent method for singly linearly constrained problems (2019)
  11. Demyanov, Vasily; Arnold, Dan; Rojas, Temistocles; Christie, Mike: Uncertainty quantification in reservoir prediction. II: Handling uncertainty in the geological scenario (2019)
  12. Devarakonda, Aditya; Fountoulakis, Kimon; Demmel, James; Mahoney, Michael W.: Avoiding communication in primal and dual block coordinate descent methods (2019)
  13. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  14. Gao, Wenbo; Goldfarb, Donald: Quasi-Newton methods: superlinear convergence without line searches for self-concordant functions (2019)
  15. García Nieto, P. J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Paredes-Sánchez, J. P.; Riesgo Fernández, P.: Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques (2019)
  16. Ge, Li; Liu, Jiaguo; Zhang, Yusen; Dehmer, Matthias: Identifying anticancer peptides by using a generalized chaos game representation (2019)
  17. Halder, Yous V.; Sanderse, Benjamin; Koren, Barry: An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses (2019)
  18. He, Jiachuan; Mattis, Steven A.; Butler, Troy D.; Dawson, Clint N.: Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines (2019)
  19. Hong, Bin; Zhang, Weizhong; Liu, Wei; Ye, Jieping; Cai, Deng; He, Xiaofei; Wang, Jie: Scaling up sparse support vector machines by simultaneous feature and sample reduction (2019)
  20. Huang, Ming; Zhuang, Fuzhen; Zhang, Xiao; Ao, Xiang; Niu, Zhengyu; Zhang, Min-Ling; He, Qing: Supervised representation learning for multi-label classification (2019)

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