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 861 articles )

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  1. Ahookhosh, Masoud; Neumaier, Arnold: An optimal subgradient algorithm with subspace search for costly convex optimization problems (2019)
  2. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  3. Baumann, P.; Hochbaum, D. S.; Yang, Y. T.: A comparative study of the leading machine learning techniques and two new optimization algorithms (2019)
  4. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  5. Devarakonda, Aditya; Fountoulakis, Kimon; Demmel, James; Mahoney, Michael W.: Avoiding communication in primal and dual block coordinate descent methods (2019)
  6. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  7. Gao, Wenbo; Goldfarb, Donald: Quasi-Newton methods: superlinear convergence without line searches for self-concordant functions (2019)
  8. Ge, Li; Liu, Jiaguo; Zhang, Yusen; Dehmer, Matthias: Identifying anticancer peptides by using a generalized chaos game representation (2019)
  9. Li, Hao-ran; He, Fa-zhi; Yan, Xiao-hu: IBEA-SVM: an indicator-based evolutionary algorithm based on pre-selection with classification guided by SVM (2019)
  10. Li, Shan; Deng, Weihong: Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition (2019)
  11. Liu, Jiapeng; Liao, Xiuwu; Kadziński, Miłosz; Słowiński, Roman: Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria (2019)
  12. Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
  13. Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin: Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach (2019)
  14. Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya: LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms (2019) arXiv
  15. Xiao, Lin; Yu, Adams Wei; Lin, Qihang; Chen, Weizhu: DSCOVR: randomized primal-dual block coordinate algorithms for asynchronous distributed optimization (2019)
  16. Abdulhussain, Sadiq H.; Ramli, Abd Rahman; Al-Haddad, Syed Abdul Rahman; Mahmmod, Basheera M.; Jassim, Wissam A.: Fast recursive computation of Krawtchouk polynomials (2018)
  17. Aggarwal, Charu C.: Machine learning for text (2018)
  18. Ah-Pine, Julien: An efficient and effective generic agglomerative hierarchical clustering approach (2018)
  19. Aravkin, Aleksandr Y.; Burke, James V.; Pillonetto, Gianluigi: Generalized system identification with stable spline kernels (2018)
  20. Beck, Amir; Pauwels, Edouard; Sabach, Shoham: Primal and dual predicted decrease approximation methods (2018)

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