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 641 articles )

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  1. 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)
  2. Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez: JaTeCS an open-source JAva TExt Categorization System (2017) arXiv
  3. 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)
  4. Budynkov, Alexey N.; Masolkin, S.I.: The problem of choosing the kernel for one-class support vector machines (2017)
  5. Demyanova, Yulia; Pani, Thomas; Veith, Helmut; Zuleger, Florian: Empirical software metrics for benchmarking of verification tools (2017)
  6. García Nieto, P.J.; García-Gonzalo, E.; Alonso Fernández, J.R.; Díaz Muñiz, C.: A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (northern Spain) (2017)
  7. Hwang, Kyoungmi; Kim, Dohyun; Lee, Kyungsik; Lee, Chungmok; Park, Sungsoo: Embedded variable selection method using signomial classification (2017)
  8. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  9. Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth: sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo (2017) arXiv
  10. Li, Genyuan; Xing, Xi; Welsh, William; Rabitz, Herschel: High dimensional model representation constructed by support vector regression. I. Independent variables with known probability distributions (2017)
  11. López-González, Gehová; Arana-Daniel, Nancy; Bayro-Corrochano, Eduardo: Parallel Clifford support vector machines using the Gaussian kernel (2017)
  12. Mendes Júnior, Pedro R.; de Souza, Roberto M.; de O. Werneck, Rafael; Stein, Bernardo V.; Pazinato, Daniel V.; de Almeida, Waldir R.; Penatti, Otávio A. B.; da S. Torres, Ricardo; Rocha, Anderson: Nearest neighbors distance ratio open-set classifier (2017)
  13. Razzaghi, Talayeh; Xanthopoulos, Petros; Şeref, Onur: Constraint relaxation, cost-sensitive learning and bagging for imbalanced classification problems with outliers (2017)
  14. Şeref, Onur; Razzaghi, Talayeh; Xanthopoulos, Petros: Weighted relaxed support vector machines (2017)
  15. Wang, Ximing; Fan, Neng; Pardalos, Panos M.: Stochastic subgradient descent method for large-scale robust chance-constrained support vector machines (2017)
  16. Wang, Xin; Ren, Yanshuang; Zhang, Wensheng: Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features (2017)
  17. Xudong Li, Defeng Sun, Kim-Chuan Toh: On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (2017) arXiv
  18. Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, Hsuan-Tien Lin: libact: Pool-based Active Learning in Python (2017) arXiv
  19. Alabdulmohsin, Ibrahim; Cisse, Moustapha; Gao, Xin; Zhang, Xiangliang: Large margin classification with indefinite similarities (2016)
  20. Bai, Yan-Qin; Shen, Kai-Ji: Alternating direction method of multipliers for $\ell_1$-$\ell_2$-regularized logistic regression model (2016)

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