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:

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  1. Ah-Pine, Julien: Learning doubly stochastic and nearly idempotent affinity matrix for graph-based clustering (2022)
  2. Alacaoglu, Ahmet; Fercoq, Olivier; Cevher, Volkan: On the convergence of stochastic primal-dual hybrid gradient (2022)
  3. Bajārs, Jānis; Kozirevs, Filips: Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model (2022)
  4. Chen, Zhen; Liu, Keyu; Yang, Xibei; Fujita, Hamido: Random sampling accelerator for attribute reduction (2022)
  5. Fan, Yiwei; Zhao, Junlong: Safe sample screening rules for multicategory angle-based support vector machines (2022)
  6. Fatone, L.; Funaro, D.; Manzini, G.: A decision-making machine learning approach in Hermite spectral approximations of partial differential equations (2022)
  7. Gorbunov, Eduard; Dvurechensky, Pavel; Gasnikov, Alexander: An accelerated method for derivative-free smooth stochastic convex optimization (2022)
  8. Jones, Corinne; Roulet, Vincent; Harchaoui, Zaid: Discriminative clustering with representation learning with any ratio of labeled to unlabeled data (2022)
  9. Kouri, D. P.: A matrix-free trust-region Newton algorithm for convex-constrained optimization (2022)
  10. Le, Trung; Nguyen, Khanh; Phung, Dinh: Improving kernel online learning with a snapshot memory (2022)
  11. Liang, Xijun; Zhang, Zhipeng; Song, Yunquan; Jian, Ling: Kernel-based online regression with canal loss (2022)
  12. Liang, Xiubo; Wang, Guoqiang; Yu, Bo: A reduced proximal-point homotopy method for large-scale non-convex BQP (2022)
  13. Liu, Chong; Wang, Yu-Xiang: Doubly robust crowdsourcing (2022)
  14. Liu, Deyi; Cevher, Volkan; Tran-Dinh, Quoc: A Newton Frank-Wolfe method for constrained self-concordant minimization (2022)
  15. Nguyen, Lam M.; van Dijk, Marten; Phan, Dzung T.; Nguyen, Phuong Ha; Weng, Tsui-Wei; Kalagnanam, Jayant R.: Finite-sum smooth optimization with SARAH (2022)
  16. Sun, Yuan; Wang, Sheng; Shen, Yunzhuang; Li, Xiaodong; Ernst, Andreas T.; Kirley, Michael: Boosting ant colony optimization via solution prediction and machine learning (2022)
  17. Thongsuwan, Setthanun; Jaiyen, Saichon: A deep single-pass learning for recognition of handwritten digits (2022)
  18. Tran-Dinh, Quoc; Pham, Nhan H.; Phan, Dzung T.; Nguyen, Lam M.: A hybrid stochastic optimization framework for composite nonconvex optimization (2022)
  19. Wang, Guanfang; Chen, Xianshan; Tian, Geng; Yang, Jiasheng: A novel (N)-gram-based image classification model and its applications in diagnosing thyroid nodule and retinal OCT images (2022)
  20. Xu, Yiming; Keshavarzzadeh, Vahid; Kirby, Robert M.; Narayan, Akil: A bandit-learning approach to multifidelity approximation (2022)

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