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 1054 articles )

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  1. Aggarwal, Charu C.: Linear algebra and optimization for machine learning. A textbook (2020)
  2. Bauermeister, Christoph; Keren, Hanna; Braun, Jochen: Unstructured network topology begets order-based representation by privileged neurons (2020)
  3. Bellavia, Stefania; Krejić, Nataša; Morini, Benedetta: Inexact restoration with subsampled trust-region methods for finite-sum minimization (2020)
  4. Chang, Chuan-Yu; Srinivasan, Kathiravan; Hu, Hui-Ya; Tsai, Yuh-Shyan; Sharma, Vishal; Agarwal, Punjal: SFFS-SVM based prostate carcinoma diagnosis in DCE-MRI via ACM segmentation (2020)
  5. Colombo, Tommaso; Sagratella, Simone: Distributed algorithms for convex problems with linear coupling constraints (2020)
  6. Fercoq, Olivier; Qu, Zheng: Restarting the accelerated coordinate descent method with a rough strong convexity estimate (2020)
  7. 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)
  8. Gustavo Henrique de Rosa, João Paulo Papa, Alexandre Xavier Falcão: OPFython: A Python-Inspired Optimum-Path Forest Classifier (2020) arXiv
  9. Heider, Yousef; Wang, Kun; Sun, WaiChing: (\mathrmSO(3))-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (2020)
  10. Hung, Ying-Chao; Michailidis, George; PakHai Lok, Horace: Locating infinite discontinuities in computer experiments (2020)
  11. Jacobs, Kayla; Itai, Alon; Wintner, Shuly: Acronyms: identification, expansion and disambiguation (2020)
  12. Jiang, Wei; Siddiqui, Sauleh: Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent (2020)
  13. Jiang, Zehua; Liu, Keyu; Yang, Xibei; Yu, Hualong; Fujita, Hamido; Qian, Yuhua: Accelerator for supervised neighborhood based attribute reduction (2020)
  14. Kwon, Yongchan; Kim, Wonyoung; Sugiyama, Masashi; Paik, Myunghee Cho: Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric (2020)
  15. Lindeberg, Tony: Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade (2020)
  16. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (2020)
  17. Lv, Didi; Zhou, Qingping; Choi, Jae Kyu; Li, Jinglai; Zhang, Xiaoqun: Nonlocal TV-Gaussian prior for Bayesian inverse problems with applications to limited CT reconstruction (2020)
  18. Mishchenko, Konstantin; Iutzeler, Franck; Malick, Jérôme: A distributed flexible delay-tolerant proximal gradient algorithm (2020)
  19. Oishi, Atsuya; Yagawa, Genki: A surface-to-surface contact search method enhanced by deep learning (2020)
  20. Park, Seonho; Jung, Seung Hyun; Pardalos, Panos M.: Combining stochastic adaptive cubic regularization with negative curvature for nonconvex optimization (2020)

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