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 728 articles )

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  1. Beck, Amir; Pauwels, Edouard; Sabach, Shoham: Primal and dual predicted decrease approximation methods (2018)
  2. Chen, Tianyi; Curtis, Frank E.; Robinson, Daniel P.: Farsa for $\ell_1$-regularized convex optimization: local convergence and numerical experience (2018)
  3. Drusvyatskiy, Dmitriy; Fazel, Maryam; Roy, Scott: An optimal first order method based on optimal quadratic averaging (2018)
  4. Francisco Charte, Antonio J. Rivera, David Charte, María J. del Jesus, Francisco Herrera: Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository (2018) arXiv
  5. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  6. García-Nieto, P.J.; García-Gonzalo, E.; Alonso Fernández, J.R.; Díaz Muñiz, C.: Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach (2018)
  7. García Nieto, P.J.; García-Gonzalo, E.; Álvarez Antón, J.C.; González Suárez, V.M.; Mayo Bayón, R.; Mateos Martín, F.: A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance (2018)
  8. Jampour, Mahdi; Moin, Mohammad-Shahram; Yu, Lap-Fai; Bischof, Horst: Mapping forests: a comprehensive approach for nonlinear mapping problems (2018)
  9. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: A highly efficient semismooth Newton augmented Lagrangian method for solving lasso problems (2018)
  10. Lukas W. Lehnert, Hanna Meyer, Wolfgang A. Obermeier, Brenner Silva, Bianca Regeling, Jörg Bendix: Hyperspectral Data Analysis in R: the hsdar Package (2018) arXiv
  11. Lu, Xiaoling; Dong, Fengchi; Liu, Xiexin; Chang, Xiangyu: Varying coefficient support vector machines (2018)
  12. Sharma, Ronesh; Bayarjargal, Maitsetseg; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok: MoRFPred-plus: computational identification of moRFs in protein sequences using physicochemical properties and HMM profiles (2018)
  13. Zhang, Lichao; Kong, Liang: iRSspot-ADPM: identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components (2018)
  14. Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general pseaac (2018)
  15. Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez: JaTeCS an open-source JAva TExt Categorization System (2017) arXiv
  16. Bacciu, Davide; Carta, Antonio; Gnesi, Stefania; Semini, Laura: An experience in using machine learning for short-term predictions in smart transportation systems (2017)
  17. Bounhas, Myriam; Prade, Henri; Richard, Gilles: Analogy-based classifiers for nominal or numerical data (2017)
  18. Brentan, Bruno M.; Luvizotto, Edevar jun.; Herrera, Manuel; Izquierdo, Joaquín; Pérez-García, Rafael: Hybrid regression model for near real-time urban water demand forecasting (2017)
  19. Budynkov, Alexey N.; Masolkin, S.I.: The problem of choosing the kernel for one-class support vector machines (2017)
  20. Demyanova, Yulia; Pani, Thomas; Veith, Helmut; Zuleger, Florian: Empirical software metrics for benchmarking of verification tools (2017)

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