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 618 articles )

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  1. 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)
  2. 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)
  3. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  4. 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)
  5. Şeref, Onur; Razzaghi, Talayeh; Xanthopoulos, Petros: Weighted relaxed support vector machines (2017)
  6. Alabdulmohsin, Ibrahim; Cisse, Moustapha; Gao, Xin; Zhang, Xiangliang: Large margin classification with indefinite similarities (2016)
  7. Bai, Yan-Qin; Shen, Kai-Ji: Alternating direction method of multipliers for $\ell_1$-$\ell_2$-regularized logistic regression model (2016)
  8. Balasubramanian, Krishnakumar; Yu, Kai; Lebanon, Guy: Smooth sparse coding via marginal regression for learning sparse representations (2016)
  9. Benedetto, John J.; Czaja, Wojciech; Dobrosotskaya, Julia; Doster, Timothy; Duke, Kevin: Spatial-spectral operator theoretic methods for hyperspectral image classification (2016)
  10. 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)
  11. Byrd, Richard H.; Chin, Gillian M.; Nocedal, Jorge; Oztoprak, Figen: A family of second-order methods for convex $\ell _1$-regularized optimization (2016)
  12. Chen, Jingying; Luo, Nan; Liu, Yuanyuan; Liu, Leyuan; Zhang, Kun; Kolodziej, Joanna: A hybrid intelligence-aided approach to affect-sensitive e-learning (2016) ioport
  13. Di Pillo, G.; Latorre, V.; Lucidi, S.; Procacci, E.: An application of support vector machines to sales forecasting under promotions (2016)
  14. Doğan, Ürün; Glasmachers, Tobias; Igel, Christian: A unified view on multi-class support vector classification (2016)
  15. Eskandari, S.; Javidi, M.M.: Online streaming feature selection using rough sets (2016)
  16. Fernández, Alberto; Elkano, Mikel; Galar, Mikel; Sanz, José Antonio; Alshomrani, Saleh; Bustince, Humberto; Herrera, Francisco: Enhancing evolutionary fuzzy systems for multi-class problems: distance-based relative competence weighting with truncated confidences (DRCW-TC) (2016)
  17. Fountoulakis, Kimon; Gondzio, Jacek: A second-order method for strongly convex $\ell _1$-regularization problems (2016)
  18. Frandi, Emanuele; Ñanculef, Ricardo; Lodi, Stefano; Sartori, Claudio; Suykens, Johan A.K.: Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee (2016)
  19. Gámez, Juan Carlos; García, David; González, Antonio; Pérez, Raúl: Ordinal classification based on the sequential covering strategy (2016)
  20. García Nieto, P.J.; García-Gonzalo, E.; Alonso Fernández, J.R.; Díaz Muñiz, C.: A hybrid PSO optimized SVM-based model for predicting a successful growth cycle of the \itSpirulina platensis from raceway experiments data (2016)

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