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 1098 articles )

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  1. Sun, Ruoyu; Ye, Yinyu: Worst-case complexity of cyclic coordinate descent: (O(n^2)) gap with randomized version (2021)
  2. Aggarwal, Charu C.: Linear algebra and optimization for machine learning. A textbook (2020)
  3. Bauermeister, Christoph; Keren, Hanna; Braun, Jochen: Unstructured network topology begets order-based representation by privileged neurons (2020)
  4. Bellavia, Stefania; Krejić, Nataša; Morini, Benedetta: Inexact restoration with subsampled trust-region methods for finite-sum minimization (2020)
  5. Blanco, Victor; Puerto, Justo; Rodriguez-Chia, Antonio M.: On (\ell_p)-support vector machines and multidimensional kernels (2020)
  6. Chan, Raymond H.; Kan, Kelvin K.; Nikolova, Mila; Plemmons, Robert J.: A two-stage method for spectral-spatial classification of hyperspectral images (2020)
  7. Chelly Dagdia, Zaineb; Elouedi, Zied: A hybrid fuzzy maintained classification method based on dendritic cells (2020)
  8. Colombo, Tommaso; Sagratella, Simone: Distributed algorithms for convex problems with linear coupling constraints (2020)
  9. Daoudi, Mohamed; Alvarez Paiva, Juan-Carlos; Kacem, Anis: The Riemannian and affine geometry of facial expression and action recognition (2020)
  10. Elman, Miriam R.; Minnier, Jessica; Chang, Xiaohui; Choi, Dongseok: Noise accumulation in high dimensional classification and total signal index (2020)
  11. Fercoq, Olivier; Qu, Zheng: Restarting the accelerated coordinate descent method with a rough strong convexity estimate (2020)
  12. García-Nieto, P. J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Alonso Fernández, J. R.; Díaz Muñiz, C.: A hybrid DE optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: a case study (2020)
  13. Gustavo Henrique de Rosa, João Paulo Papa, Alexandre Xavier Falcão: OPFython: A Python-Inspired Optimum-Path Forest Classifier (2020) arXiv
  14. Halbersberg, Dan; Wienreb, Maydan; Lerner, Boaz: Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier (2020)
  15. Heider, Yousef; Wang, Kun; Sun, WaiChing: (\mathrmSO(3))-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (2020)
  16. Hung, Ying-Chao; Michailidis, George; PakHai Lok, Horace: Locating infinite discontinuities in computer experiments (2020)
  17. Jacobs, Kayla; Itai, Alon; Wintner, Shuly: Acronyms: identification, expansion and disambiguation (2020)
  18. Jiang, Wei; Siddiqui, Sauleh: Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent (2020)
  19. Jiang, Zehua; Liu, Keyu; Yang, Xibei; Yu, Hualong; Fujita, Hamido; Qian, Yuhua: Accelerator for supervised neighborhood based attribute reduction (2020)
  20. Kwon, Yongchan; Kim, Wonyoung; Sugiyama, Masashi; Paik, Myunghee Cho: Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric (2020)

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