LIBSVM

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: http://dl.acm.org/citation.cfm?id=1961199


References in zbMATH (referenced in 765 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.: FarRSA 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. Fang, Le-Heng; Lin, Wei; Luo, Qiang: Brain-inspired constructive learning algorithms with evolutionally additive nonlinear neurons (2018)
  5. Fawzi, Alhussein; Fawzi, Omar; Frossard, Pascal: Analysis of classifiers’ robustness to adversarial perturbations (2018)
  6. Fountoulakis, Kimon; Tappenden, Rachael: A flexible coordinate descent method (2018)
  7. 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
  8. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  9. 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)
  10. 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)
  11. Hwang, Sangheum; Jeong, Myong K.: Robust relevance vector machine for classification with variational inference (2018)
  12. Jampour, Mahdi; Moin, Mohammad-Shahram; Yu, Lap-Fai; Bischof, Horst: Mapping forests: a comprehensive approach for nonlinear mapping problems (2018)
  13. Jia, Cangzhi; Yang, Qing; Zou, Quan: NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC (2018)
  14. Laube, Pascal; Franz, Matthias O.; Umlauf, Georg: Learnt knot placement in B-spline curve approximation using support vector machines (2018)
  15. Lin, Dongyun; Sun, Lei; Toh, Kar-Ann; Zhang, Jing Bo; Lin, Zhiping: Twin SVM with a reject option through ROC curve (2018)
  16. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: A highly efficient semismooth Newton augmented Lagrangian method for solving lasso problems (2018)
  17. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: On efficiently solving the subproblems of a level-set method for fused lasso problems (2018)
  18. Lukas W. Lehnert, Hanna Meyer, Wolfgang A. Obermeier, Brenner Silva, Bianca Regeling, Jörg Bendix: Hyperspectral Data Analysis in R: the hsdar Package (2018) arXiv
  19. Lu, Xiaoling; Dong, Fengchi; Liu, Xiexin; Chang, Xiangyu: Varying coefficient support vector machines (2018)
  20. Morinaga, Atsushi; Hara, Kenji; Inoue, Kohei; Urahama, Kiichi: Classification between natural and graphics images based on generalized Gaussian distributions (2018)

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