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 629 articles )

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  1. Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez: JaTeCS an open-source JAva TExt Categorization System (2017) arXiv
  2. 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)
  3. Budynkov, Alexey N.; Masolkin, S.I.: The problem of choosing the kernel for one-class support vector machines (2017)
  4. Demyanova, Yulia; Pani, Thomas; Veith, Helmut; Zuleger, Florian: Empirical software metrics for benchmarking of verification tools (2017)
  5. 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)
  6. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  7. 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)
  8. López-González, Gehová; Arana-Daniel, Nancy; Bayro-Corrochano, Eduardo: Parallel Clifford support vector machines using the Gaussian kernel (2017)
  9. Razzaghi, Talayeh; Xanthopoulos, Petros; Şeref, Onur: Constraint relaxation, cost-sensitive learning and bagging for imbalanced classification problems with outliers (2017)
  10. Şeref, Onur; Razzaghi, Talayeh; Xanthopoulos, Petros: Weighted relaxed support vector machines (2017)
  11. Wang, Ximing; Fan, Neng; Pardalos, Panos M.: Stochastic subgradient descent method for large-scale robust chance-constrained support vector machines (2017)
  12. Alabdulmohsin, Ibrahim; Cisse, Moustapha; Gao, Xin; Zhang, Xiangliang: Large margin classification with indefinite similarities (2016)
  13. Bai, Yan-Qin; Shen, Kai-Ji: Alternating direction method of multipliers for $\ell_1$-$\ell_2$-regularized logistic regression model (2016)
  14. Balasubramanian, Krishnakumar; Yu, Kai; Lebanon, Guy: Smooth sparse coding via marginal regression for learning sparse representations (2016)
  15. Benedetto, John J.; Czaja, Wojciech; Dobrosotskaya, Julia; Doster, Timothy; Duke, Kevin: Spatial-spectral operator theoretic methods for hyperspectral image classification (2016)
  16. Bloom, Veronica; Griva, Igor; Quijada, Fabio: Fast projected gradient method for support vector machines (2016)
  17. Borzov, Sergey Mikhailovich; Mel’nikov, Pavel V.; Pestunov, Igor Alexeevich; Potaturkin, Oleg Iosifovich; Fedotov, Anatolii Mikhailovich: Integrated processing of hyperspectral images on the basis of spectral and spatial information (in Russian) (2016) ioport
  18. Byrd, Richard H.; Chin, Gillian M.; Nocedal, Jorge; Oztoprak, Figen: A family of second-order methods for convex $\ell _1$-regularized optimization (2016)
  19. Chen, Jingying; Luo, Nan; Liu, Yuanyuan; Liu, Leyuan; Zhang, Kun; Kolodziej, Joanna: A hybrid intelligence-aided approach to affect-sensitive e-learning (2016) ioport
  20. Di Pillo, G.; Latorre, V.; Lucidi, S.; Procacci, E.: An application of support vector machines to sales forecasting under promotions (2016)

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