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 1184 articles )

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  1. Ah-Pine, Julien: Learning doubly stochastic and nearly idempotent affinity matrix for graph-based clustering (2022)
  2. Chen, Zhen; Liu, Keyu; Yang, Xibei; Fujita, Hamido: Random sampling accelerator for attribute reduction (2022)
  3. Jones, Corinne; Roulet, Vincent; Harchaoui, Zaid: Discriminative clustering with representation learning with any ratio of labeled to unlabeled data (2022)
  4. Kouri, D. P.: A matrix-free trust-region Newton algorithm for convex-constrained optimization (2022)
  5. Liang, Xijun; Zhang, Zhipeng; Song, Yunquan; Jian, Ling: Kernel-based online regression with canal loss (2022)
  6. Liang, Xiubo; Wang, Guoqiang; Yu, Bo: A reduced proximal-point homotopy method for large-scale non-convex BQP (2022)
  7. Liu, Chong; Wang, Yu-Xiang: Doubly robust crowdsourcing (2022)
  8. Tran-Dinh, Quoc; Pham, Nhan H.; Phan, Dzung T.; Nguyen, Lam M.: A hybrid stochastic optimization framework for composite nonconvex optimization (2022)
  9. Xu, Yiming; Keshavarzzadeh, Vahid; Kirby, Robert M.; Narayan, Akil: A bandit-learning approach to multifidelity approximation (2022)
  10. Yang, Zhen-Ping; Lin, Gui-Hua: Two fast variance-reduced proximal gradient algorithms for SMVIPs -- stochastic mixed variational inequality problems with suitable applications to stochastic network games and traffic assignment problems (2022)
  11. Zhou, Yi; Liang, Yingbin; Zhang, Huishuai: Understanding generalization error of SGD in nonconvex optimization (2022)
  12. Atarashi, Kyohei; Oyama, Satoshi; Kurihara, Masahito: Factorization machines with regularization for sparse feature interactions (2021)
  13. Bian, Fengmiao; Liang, Jingwei; Zhang, Xiaoqun: A stochastic alternating direction method of multipliers for non-smooth and non-convex optimization (2021)
  14. Blanchard, Gilles; Deshmukh, Aniket Anand; Dogan, Urun; Lee, Gyemin; Scott, Clayton: Domain generalization by marginal transfer learning (2021)
  15. Brust, Johannes J.; Di, Zichao (Wendy); Leyffer, Sven; Petra, Cosmin G.: Compact representations of structured BFGS matrices (2021)
  16. Burkina, M.; Nazarov, I.; Panov, M.; Fedonin, G.; Shirokikh, B.: Inductive matrix completion with feature selection (2021)
  17. Curtin, Ryan R.; Edel, Marcus; Prabhu, Rahul Ganesh; Basak, Suryoday; Lou, Zhihao; Sanderson, Conrad: The ensmallen library for flexible numerical optimization (2021)
  18. Galvan, Giulio; Lapucci, Matteo; Lin, Chih-Jen; Sciandrone, Marco: A two-level decomposition framework exploiting first and second order information for SVM training problems (2021)
  19. Gossmann, Alexej; Pezeshk, Aria; Wang, Yu-Ping; Sahiner, Berkman: Test data reuse for the evaluation of continuously evolving classification algorithms using the area under the receiver operating characteristic curve (2021)
  20. Gower, Robert M.; Richtárik, Peter; Bach, Francis: Stochastic quasi-gradient methods: variance reduction via Jacobian sketching (2021)

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